Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access


Research Article

Co-Authorship and Bibliographic Coupling Network Effects on Citations

* E-mail: [email protected]

Affiliations Department of Economics, Ca' Foscari University of Venice, Venice, Italy, Department of Management, Ca' Foscari University of Venice, Venice, Italy, Institut für Organization und Globale Managementstudien, Johannes Kepler Universität, Linz, Austria

Affiliation Department of Economics, Ca' Foscari University of Venice, Venice, Italy

  • Claudio Biscaro, 
  • Carlo Giupponi


  • Published: June 9, 2014
  • Reader Comments

Figure 1

This paper analyzes the effects of the co-authorship and bibliographic coupling networks on the citations received by scientific articles. It expands prior research that limited its focus on the position of co-authors and incorporates the effects of the use of knowledge sources within articles: references. By creating a network on the basis of shared references, we propose a way to understand whether an article bridges among extant strands of literature and infer the size of its research community and its embeddedness. Thus, we map onto the article – our unit of analysis – the metrics of authors' position in the co-authorship network and of the use of knowledge on which the scientific article is grounded. Specifically, we adopt centrality measures – degree , betweenneess , and closeness centrality – in the co-authorship network and degree , betweenness centrality and clustering coefficient in the bibliographic coupling and show their influence on the citations received in first two years after the year of publication. Findings show that authors' degree positively impacts citations. Also closeness centrality has a positive effect manifested only when the giant component is relevant. Author's betweenness centrality has instead a negative effect that persists until the giant component - largest component of the network in which all nodes can be linked by a path - is relevant. Moreover, articles that draw on fragmented strands of literature tend to be cited more, whereas the size of the scientific research community and the embeddedness of the article in a cohesive cluster of literature have no effect.

Citation: Biscaro C, Giupponi C (2014) Co-Authorship and Bibliographic Coupling Network Effects on Citations. PLoS ONE 9(6): e99502.

Editor: Christos A. Ouzounis, The Centre for Research and Technology, Hellas, Greece

Received: February 7, 2014; Accepted: May 15, 2014; Published: June 9, 2014

Copyright: © 2014 Biscaro, Giupponi. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors gratefully acknowledge the financial support of the KULTURisk Project (FP7-ENV.2010.1.3.2-1-265280). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


Generating scientific knowledge is as a social activity in which scientists find problems to tackle, become aware of connections between elements, elaborate on existing ideas to produce new or refined answers. It is a recipe with both social and knowledge ingredients. However, studies on the impact of scientific knowledge have mostly stressed either the social or the knowledge part of the story, neglecting their interaction.

Studies on co-authorship networks have gathered the attention of many scholars, not just because of their descriptive and synthetic power to describe the evolution of research communities, but also because social networks play a significant role on the generation of knowledge [1] – [3] . Social interactions still play a major role even in an era in which knowledge is accessible on-line [4] , [5] , in contrast of what common sense would suggest. Co-authorship networks (hereafter CA) are a type of social network based on co-author relationships that are built over time by scientists.

The increasing number of researches that link CA and scientific impact – citations – reminds us that collaboration and its structure profoundly affect the quality of work. In this respect, scholarly attention increased towards the properties of CA and their effects on citations [2] , [6] , [7] . Newman explored the advantage of the first publications in a field [8] , Mazloumian and colleagues documented the bandwagon effect of accolades, world-class recognitions, and landmark papers on prior works of the rewarded scientists [9] . With respect to the network position of co-authors, centrality measures significantly correlate with the article citation count [7] . In particular, degree centrality – that counts the number of personal contacts of an author – and betweenness centrality [2] – that measures how many shortest paths connecting any two authors in the dataset run through any single node.

Co-authorship relations represent the social side of the generative activity of scientists. For individuals, whose main capital and product is knowledge, social interactions are a crucial to improve the quality of their work. They are so relevant that opportunities of unintentional interaction are favored (or even forced) in some contexts oriented to knowledge generation, such as high-tech firms [10] , [11] . The reason is that the quality of ideas and knowledge work is given not just by the number of people who create it, but mainly by the knowledge diversity to which they are exposed. Establishing social connections with diverse groups enables the exposure to multiple and different intellectual domains, methods, perspectives and techniques [12] , [13] and the inclusion of “whole domains of elements … into the combinative hopper” (Simonton, 1995: 473). Recent works showed that also in different fields of science the attitude towards interdisciplinary is rewarded with more citations [2] , [7] , [15] .

We need a different lens to overcome the paradox in which studies on knowledge generation are mainly carried out by studying the social component, neglecting the most fundamental one: knowledge itself. After all, even the social part of the theory assumes that it is by combining distant or multiple domains of knowledge that new and impactful ideas are generated. Yet, knowledge remains just an outcome. We think that the bibliographic coupling network is the appropriate lens to look at how knowledge is combined into a scientific work. The bibliographic coupling network (hereafter BC) is constructed on the shared references among publications [16] , thus it provides deeper insights on the scientific activity, as it reveals information on how authors use and construct links among the existing literature. It can be used to analyze the articles' position within the literature, to infer the size of its research community and it may help answer conjectures that have not yet been addressed.

Indeed, the idea of analyzing the knowledge used by authors is not revolutionary, and it could be traced back at least to the intuition of John of Salisbury, then quoted by Newton who wrote that he was able to see further as he stood on giants' shoulders. This inspiring portrait of a scientist who builds on other scientists' work reveals that scientific work is in an ongoing evolution and there exist a real (and a spiritual) connection among scientists. Along the thread of scientific evolution, Polanyi [17] noticed that scientists adjust their efforts on the basis of “the hitherto achieved results of the others” (p. 2) thus creating a continuum of different works that results in a continuous progress that links different scientific domains and whereby the key knowledge sources change over time [18] – [22] .

For this reason, we want to incorporate the analysis of the use of knowledge sources – the references – into the more traditional variables that look at the social interaction in order to study the effects of both social and knowledge networks on the scientific impact of knowledge production. Thus, we integrate metrics of BC and CA to see their effect on articles' citations. Thereby, as we follow this intent, we align with the recent work of Uddin, Hossain and Rasmussen [2] , as our unit of analysis is the publication whereby we map metrics from the two network.

To better capture network effects, we isolate papers of a specific literature of vulnerability in climatic change and exclude those outside that literature, whose citations could have a different distribution [23] , [24] . We do that by performing an unsupervised textual analysis and categorization of all papers into topics [25] – [27] : this step also allows us to identify elements of originality and innovation in order to control for advantages coming from the introduction of new research topics or new combinations of topics. Then, we construct the CA and BC network [28] , to answer the following research questions: (1) How does the co-authorship network structure influence the scientific impact of an article in terms of citations? (2) How do the article's knowledge sources influence its scientific impact?

In this work we use the terms paper, article, publication and work interchangeably. Sometimes the term node or vertex will be referred to the author/co-author or to the paper/article according to the network under scrutiny is the CA or the BC, and, similarly, the term tie or edge will be referred to the co-authorship relation or to the fact that two articles share at least one reference.

The rest of our article proceeds with the description of the methods with which we constructed the dataset and addressed our research questions, than we describe the measures we used and the theoretical reasons underpinning their adoption. Next, we will discuss our methods and research setting. After that we will present the results of our analysis and we will conclude deriving general theoretical contributions.

To test our arguments, we need a set of articles that originated within a coherent body of literature. Thus, after downloading the dataset, our first intent is consolidate the set by screening out those publications which entered our search due to the fact that the multiple keyword combinations adopted could in part be used also in contexts far from our interests, like the medical one. We carry out a machine learning classification task [25] , [27] , [29] on the textual information (more information both on method and on the process is contained in File S1 ), and isolate only the publications coherent with the identified literature. Only then, we generate two types of networks based on scholarly collaborations, and on articles' references.

The bibliographic coupling and co-authorship networks

The structure of a scientific literature and that one of collaboration between authors can be analyzed through networks that are based on the mathematical mapping of relations (edges) between dyads of elements (vertices or nodes).

From a set of scientific articles, it is possible to establish relations among different attributes, e.g., references, co-authors, keywords. In this research, we focus on two types of networks: the co-authorship network (CA), and the bibliographic coupling network (BC). In the BC, the articles are the vertices and an edge is established when they have at least one shared reference. Analogously in the co-authorship (CA) network, vertices are the authors and the edges are established between vertices who co-authored an article. We treat the BC as unweighted: the weight of the edge is not affected by the number of shared references.

In Figure 1 , we represent how to construct the two networks starting from a set of three articles i , j , and k . With respect to the CA, the activity of co-authoring both in i and j , enables author α to span across two sets of co-authors β, ω and τ (through article i ) and γ (through article j) .


  • PPT PowerPoint slide
  • PNG larger image
  • TIFF original image

Generally the choice on networks of citations privileges the choice of visualizing the links among citing and cited articles. This type of network, called co-citation, provides a picture that changes in time and in which there are as many nodes as the number of citing and cited articles. To have a more compact and time-independent network, we choose to establish relations among those articles in the dataset that share references, as shown in Figure 1 , where two same references are contained in articles i , j , while j and k share one. Thus, the BC contains only three nodes and two edges. Node j is linked with and i , and with k on the basis of the shared references, while there is no edge between i and k .

The choice of BC has also another key advantage with respect to the co-citation network. Other than compactness of the network (3343 nodes versus over 150,000), the relations that the node (article) establishes with the rest of the network (with the extant literature) is grounded in what the authors decided to include in their reference list. Thereby it is established by means of authors' choices. One of the disadvantages of the BC is that the number of relations depends on the size of the reference list, giving a positive bias towards those articles that have a larger reference list. However, we will control for it as described in the § Measures.

Data Source

As the scientific impact is dependent on the stream of literature, we build the dataset through a sequence of steps aimed at gathering a homogenous population of articles in terms of thematic structure, information on references and co-authors. The first step consists in the identification of literature streams and it is done through an accurate selection of keywords reported in Table 1 . The choice of keywords has dealt with the concept of vulnerability that is a crucial notion for several research communities: particularly for climate change adaptation and disaster risk reduction. A positive note on the literature we chose is that the meaning of vulnerability varies in the diverse research streams, which developed in isolation. Only recently, under the push of international bodies (such as IPCC and UNISDR), these fragmented scientific streams are unifying their glossary. Thereby, this is a rich context whereby to test our approach. The focus of scientists spans over a multitude of aspects that vary from strategies to adapt and anticipate possible consequences of a natural extreme event to strategies to better cope with their effects in a multitude of different social or ecological systems, affected by diverse types of natural events (floods, draughts, storms, heat waves, and extreme wind, among the others), and mediated by various morphological, geological, and social conditions.


The search, performed on April 30 2013, in the ISI Web of Knowledge returned 5585 papers published between 1985 and 2013.

We refine the dataset in three steps. We select articles that have at least two entire years of forward citations (thus we exclude articles published after 2010), belong to the same scientific ‘macro-discipline’ (the entire process of data analysis is summarized in Figure 2 ), and have references.


First, we discard the articles published after the 2010, because they do not have two complete years of citation history that is the measure of scientific impact chosen. We choose a 2 year time window as all articles belong to the same disciplinary area and we know that they should have analogous citation patterns.

Second, using the Stanford Topic Modeling suite, we classify articles based on the latent structure of topics extracted from the textual information available from the search: abstracts and titles (we do not consider keywords, as not all journals require them). The algorithm needs to arbitrarily set the number of topics. It is set to 75, a number that takes into account the large variability of the themes discussed in the field: types of environments, receptors, and units of analysis. Topics are coded by the second author of this paper, who is expert in the field (with more than three publications), and labeled on the basis of their 20 most likely words (see the SI for details). Such coding is then validated by two other experts, who also identify nine non-relevant topics. This leads to the exclusion of 270 articles whose content – topic proportion >0.5 – falls in one of these nine topics (see the SI for details).

Third, we exclude articles for which it is not possible to compute the BC network for having no references.

This process brings the dataset to a population of 3343 articles published between 1989 and 2010. We then create a series of BC and CA networks on temporal slices that keep fixed the first observation in the dataset and move forward by one year (e.g., 1989–2002, 1989–2003, 1989–2004).The temporal slices enable us to observe the position of the authors in the CA and the article in the BC at the time of the article publication. For each network slice, we extract our independent variables by means of the library ‘igraph’ [30] running in the statistical software R. Last, we performed the statistical analysis by means of negative binomial regressions. In Figure 3 , we provide a visual conceptualization of our research questions.


Starting from the article i written by α,β and γ and published in year t, two networks are derived. On the left side, the co-authorship network shows that α is already part of a group of other authors and had joint works with two of them (represented by the two edges inside the author set). β and γ are connected to the set of authors through α by means of their new joint-work i . We then extract three measures, degree, closeness and betweenness for each co-author of i , and map only the maximum value into i . On the right side, the article i have common references with articles of the sets A and B. We measure the degree, betweenness centralities and clustering coefficient of the article i . We construct the dependent variable cumulating the citations received by article i in the two years following the year of its publication. Last, we verify the effects the co-authorship and bibliographic coupling networks measures on the citation count of article i .

Scientific impact.

The dependent variable is the articles' scientific impact measured by the number of citations received (hereafter citation count ) within a two-year fixed time window starting after the year of publication. We decide to exclude the citations received during the year of publication, as that would create a bias in favor of the articles published early in the year. A fixed time window to measure citations has been chosen for several reasons: it is shown to be a good measure to determine the scientific impact within a discipline [23] ; it is not biased in favor old articles that can be cited for a longer period as it does not fully reflect the ranking of the most cited overall (0.68 spearman correlation coefficient with the cumulative citations received in April 2013); and with respect to the yearly citation average, efficiency [31] , it has the advantage to be limited in time, therefore it focus only on the immediate impact of the social activity of co-authors and positioning of the article, while being not affected by phenomena such as drifts in the literature that could impact citations but are also slower to occur. However, the two-year time window is correlated with efficiency (0.84 with p<0.001).

Degree centrality is the count of the first neighbors of a node. In the CA, it equates with the number of co-authors with whom any author has collaborated at the end of year t (hereafter author degree) . We normalize the count in the BC network, as we use the degree proportion that is the degree centrality of article i divided by the number articles cited by i . Thereby keeping constant the degree of article i , its degree proportion will decrease with the size of its reference list.

research paper on bibliographic coupling

An author i with a high betweenness centrality score acts as the shortest path between many other actors, thus potentially benefiting from brokering advantages, especially when the other actors are disconnected if node i is removed.

Clustering coefficient of a node i is the number of f neighbors of i that are connected between each other divided by the number of pairs of neighbors of i . The measure, computed only for the BC networks, captures the embeddedness of an article in the existing literature. High values of clustering coefficient show that the articles with which i shares references, also share references among them.

Our unit of analysis is the article, therefore we retrieve the only the maximum value for each measure of the CA networks as they represent the value of the most influential co-author who transfers the highest value of authority to the paper [9] .

All metrics are computed on yearly slices to capture the values at the year of publication.

Control variables

Topic generation..

An article generates a topic when the proportion of text attributed to a topic is for the first time larger than 25%. When in the same year and in the same topic, the threshold is surpassed by multiple articles, all of them are classified as topic-generating. For example, Strzepek et al. [32] and Jose et al. [33] brought in the same year the concept of vulnerability to the context of water resources and river basins and we classify both as topic generating. Their proportion of words related to the topic of water resources and river basins are beyond the threshold and there is no prior article that accomplishes that. A similar operationalization has been performed by Kaplan and Vakili [34] who considered a less conservative threshold of 20%. Topic generation is a dummy variable that assumes value 1 when an article generates a new topic and 0 otherwise. With this variable, we control for those papers that introduce a certain topic in the field that could be the most important and be rewarded by higher citations [35] , almost regardless of their quality [8] . By opening a new research stream, they benefit from a citation advantage over the followers as they lead future research that will necessarily cite them.

New topic combination.

Similarly to topic generation, articles introduce a new topic combination when they establish a combination of topics that was not present in the dataset until the year of publication. We adopt the same rule used to attribute topic generation: a topic proportion beyond the threshold of 25% determines the presence of that topic in the article and the rule of the multiple attribution of a topic combination applies similarly to topic generation : all those articles that introduce the same topic-combination in the same year are identified as introducing a new topic combination. Topic combination controls for the effect of those articles which generate a combinatorial type of innovation [36] at the level of topic.

For robustness, results do not significantly change when checked with thresholds of .20 and .30.

Size of the citing literature.

Citations depend on the size of the universe of article from which citations are drawn; therefore there is a need to control for the size of this expanding universe. As a proxy of the expanding universe of articles, we take the number of articles in the dataset two years after the year of publication of each article. Although we recognize that this universe must not be exact universe of citing articles, it provides the sense of growing attention towards the topic of vulnerability and, secondly, it is a monotonically growing body of scientific literature, such as the entire universe of scientific literature. For robustness, we also tried two different versions of the metric: (1) we inflated the measure with a relatively large fixed number representing the articles outside the dataset (10,000) that could be interested in referring to articles within; (2) we increased that fixed number (10,000) by a 4.1% each year as it is a plausible rate of growth of scientific articles in the period between 1990–2007 [37] . Results proved to be robust.

Number of authors.

The number of authors is positively correlated with the citation count , see Table 2 , and there may be several reasons: a paper with multiple co-authors is more likely to be more complex in terms of knowledge sources, as it required the work of multiple actors; the quality of the content can also be enhanced by the labor limae that can be performed by multiple hands; furthermore, the dissemination of the ideas included in the article can spread in the co-authorship network starting from multiple starting nodes. Thus, we sift out the effect by including the number of authors in the subsequent statistical analysis.


Experience in the field.

Experience is associated with a higher level of specialization, knowledge of the relevant problems in the field and with a deeper ability in publishing and diffusing ideas [38] . Thereby expert authors have both cognitive and reputation advantages. They are better known within the field, have consolidated relationships in the research community, and know how to make networking better than their less-expert peers. This should translate into higher quality production and more effective dissemination. We operationalize the experience in the field for any article i by measuring the number of articles in the dataset published by each co-author prior to the publication of i . Among these figures, we chose the maximum, as we think that it is the most expert author who has the highest influence on the paper's impact.

Citing review bias.

In several scientific disciplines there is a high concentration of reviews among the most cited articles [1] . Notwithstanding the difficulty to discriminate between review and non-review papers based solely on the number of references, because to introduce a new conceptual framework to analyze new data, often authors draw on multiple contributions in the extant literature, yet, to control for the citing bias towards articles with a large number of references, we clustered articles in two groups (review, non-review) based on the number of article cited in their reference list. The two groups have profoundly different means (µ review ≈37, (µ non-review ≈106), thus we created a dummy variable with value 1 if the article is a review, 0 otherwise.

Bandwagon effect.

Citations are boosted by the peer recognition of the author. As already noticed, there is an effect of world-class recognitions and landmark papers on the citations of both prior and subsequent works of the authors [9] . With no availability of data on scientific prizes and accolades for the authors, we decided to control for the effect of peer-recognition by identifying the 309 authors of the articles that were most cited at the end of 2012 (top 1%, 33 articles), and put a dummy variable on the 199 articles written by them in the period starting two years before their landmark paper, with the exclusion of their landmark one. The two year time-window, in which we count citations, makes articles written before that period unaffected by posterior success, as the citing authors should not be probably aware of subsequent success (except for circulating working papers that we cannot control).

To understand the impact of the variables on the citations count, we perform negative binomial regressions – a regression model specific for count data in which the dependent variable has overdispersion – with nested sets of regressors. Results are presented in the next section.

In this section, we first provide an overview of the data, and then we answer the research questions and show how the citation count is affected by the structure and positions of co-authors within the co-authorship network, and by the position of the article in the literature.

Summary statistics

After a handful of publication in the early 1990s, the literature on vulnerability increases steadily over the years, as displayed in Figure 4 (left). From 2004, the number of papers increases in a steep-log phase. Analogous is the trend of co-authors displayed in Figure 4 (right). In dark grey the cumulative number of authors in the dataset, while in light grey the number of new authors entering in the dataset at any year. Most articles are written by 3 authors (µ = 3.404, σ = 3.021, min  = 1, max  = 57), they have on average 48.65 references (σ = 34.537, min  = 1, max  = 398), and receive on average 4.95 citations in the first two years after the publication (σ = 9.890, min  = 0, max  = 273), however the distribution of citations is skewed to the right.


On the left part of the figure, the dark line represents the cumulative number of authors who at least have one publication in the dataset, while the light line represents the number of new authors.

For the non-normal distribution of most variables in the study, we use Spearman correlation to compute the correlation coefficients between the citation count and the independent variables, as shown in Table 2 . Correlations show that citation count is positively associated with most independent variables with the expection of author closeness . The negative correlation between Author closeness is particularly interesting and needs further analysis, because it means that the impact of the paper seems to be negatively related to the proximity of its authors to all other authors in the network. As expected, instead, author degree and author betweenness co-vary with the citations received by the articles, thereby connecting distant authors and having a large number of co-authors co-occur with higher citations, and they are also positively correlated (0.81 with p<0.001), as it occurs in most networks [39] .

We also find that article degree proportion , article betweenness and article clustering coefficient are positively correlated with the citations received (.22 and .30 respectively with p<.001) and between each other (.70 with p<.001). The article degree proportion 's value show that there is a link between size of the research community to which the article belongs and its citation count , and article betweenness correlation show that bridging fragmented strands of literature usually signals an increase in the citation count . The positive value between article clustering coefficient and citation count shows that being embedded in a literature co-varies with citations. Also control variables such as the number of authors , the size of literature and the author ' s experience are positively and significantly correlated with the citation count.

In the next paragraph we present the results of the regressions computed on three models. In model 1, we replicate a part of a recent study that analyzed the structural effect of the co-authorship network on the citation count [2] . In model 2, we add the BC measures, whereas model 3 is generated on a smaller dataset that considers articles published between 2008 and 2010, a period in which the giant component – the largest component of the network in which all nodes can be linked by a path – in co-authorship network connects a relevant portion of the nodes. To control for outliers, we exclude the first three observation for their abnormally large score of Author closeness that is due to the number of nodes in the CA (the first three observations take values of 1 and .33, whereas the fourth largest observation of .038 – as shown in figures S1 and S2 in File S1 ).

All regressions are performed with the negative binomial regression model that is appropriate for count-data models and has no specific assumption on the dependent variable, unlike the Poisson and the zero-inflated Poisson. Poisson assumes that the mean and the variance of the dependent variable should be equal, while in our dataset they differ significantly (µ = 4.952, σ 2  = 97.8116). Nonetheless the large number of articles in the dataset whose citation count takes value 0, we still prefer the negative binomial model to a zero-inflated Poisson, because the latter model assumes that many of the observations that take value zero are drawn from a different distribution in which articles will never be cited. In our case, there is no theoretical reason to assume that non cited articles come from a different distribution. Results are displayed in Table 3 .


The impact of co-authors' network position on the article's citation

Author degree has a positive highly significant but very small coefficient (between 0.02 and 0.01) throughout the three models. Such a positive association on citations remains when we add also the network metrics involving the shared references (in model 2), and is robust also in the smaller dataset comprising the articles published between the 2008 and 2010. This result shows that articles written by authors who have established more co-authorship relations (with other authors in the dataset) tend to be cited more. It must be noted that this cannot be a strategy pursued by authors, as the increase in citations generated by each relation is extremely small. Surprisingly, and unlike other studies [2] , [7] , author betweenness is negatively and significantly associated with the citations received by the paper as long as the giant component connects a relevant part of the authors in the dataset (model 1 and 2 versus model 3). The coefficient is large, because the normalized betweenness scores take extremely small values (min 0, max 0.025, with a right skewed distribution in which 75% of the values are below 6 · 10 −7 ). The intuition is that bridging between groups of co-authors is negatively associated to citation count . It is not clear the reason underpinning such a result: it could be due to the experience of the bridging author and to the mathematical construction of the measure of betweenness. Regarding the experience of the bridging author, research in cognitive science says that a long and vast experience is necessary to successfully put together different pieces of knowledge [40] , [41] , thereby we could think there may be some interaction between betweenness and authors' experience. Moreover the mathematical representation of the metric of betweenness overemphasizes the size of the population groups that are linked, while neglecting the redundancy of edges, thereby not capturing the number of different knowledge bases. For example, assuming that different groups have different expertise, betweenness does not distinguish if an author creates a bridge between two numerous groups (two knowledge domains), or alternatively more groups (more knowledge domains) but less numerous. Furthermore betweenness is equally sensitive to nodes directly and indirectly connected to the author, thereby it is also dependent to the size of the component. Thereby one author's betweenness score is high if she sits in a large network, despite that only a very limited number of them are directly in contact. For this reasons, we believe that author betweenness becomes not significant when we reduce the observation to those in presence of a relevant giant component, see Table 4 . Although the score is high, it does not reflect the personal benefit that a person can acquire from sitting in a position more ‘in between’ within the network. Results then suggests us not to be conclusive in our conjectures and instead suggest carrying out further analysis to understand whether this or other measures should be used to capture the idea of creating bridges across groups.


Results of model 1 and 2 suggest that there is no association between author closeness , i.e., the proximity of an author to all others in the network, and the citation count . However, author closeness is sensitive to the structure of the CA. Initially the lack of collaboration between groups does not generate a unified network structure and authors cannot use their network of relations to usefully disseminate their work. Closeness plays a positive role in the presence of a tangible giant component (see Table 4 ). Model 3 shows the results of the regression on the subset of 1683 articles published between 2008 and 2010 where author closeness 's coefficient becomes positive and significant (3161 with p-value  = 0.0004), proving that closeness is positively associated with citation count , and this may be due to the fact that direct forms of social interaction – co-authorships – facilitate the diffusion of ideas.

In summary, data support the idea that authors benefit from their accumulated co-authorship relations, and that being embedded (better when in the core rather than in the periphery) in a large network boosts the citation count of the paper. Betweenness centrality instead appears to be a problematic measure to analyze the combination of knowledge occurring in the single paper.

The impact of the bibliographic coupling network

Model 2 shows that article betweenness is positively associated to the citation count (0.82, p-value <0.01) and highlights the scientific value of those works that find connections among others that are already present in the literature. It draws the attention to the fact that looking for relationships among theoretical arguments and finding connections with remotely connected or yet disconnected knowledge domains and theories is rewarded in terms of citations, even in the short term of two years. When we restrict the analysis to the articles written from 2008 to 2010, and the giant component comprises over 90% of nodes, it seems less obvious the advantage to find new ties with the existing literature, moreover there are fewer groups of nodes to incorporate in the main component. Thus the effect of article betweenness on citations becomes not significant when the giant component in the BC absorbs most nodes, as shown both in the regression of model 3 and in Figure 5 .


Nodes are represented by articles in the dataset and the edges link articles which share one or more references. In this network, we show only articles with at least 10 citations and that are connected to the giant component (1164 nodes in 2010 that are 72,48% of amount of articles with more than 10 citations).

Similarly, article clustering coefficient shows that being embedded in a literature benefit the citation count of the article (0.30 with p-value <0.05), but the effect disappears when computed for the articles written after 2008. One possible reason is that an increasing homogeneity of articles' references could be forced by exogenous pressures thus reducing the positive effect of the embeddedness. This hypothesis could be reinforced by the call for integration of the concept of vulnerability among various streams of literatures made by international bodies of research such as UNISDR and IPCC.

Article degree proportion , a proxy of the size of the research community, does not significantly impact the citation count . Thus we can conclude that, at least in this dataset, sharing references with a large number of other articles in the extant literature is not a practice that increases the number of citations received, suggesting that recognizing arguments already well spread in the literature by acknowledging very popular prior research, as well as the size of the research community, have no apparent relation with the scientific impact. Therefore, these practices that are typical of positioning a paper within a literature sort no significant effect at the citation side.

Curiously, among the control variables, topic generation is negatively associated to the number of citations, although in model 2 the p-value is slightly larger than 10%, while there is no effect for the new topic combination . The result of a new topic is not due to a different distribution of citations over the years (see SI), but it may be due to the fact that when vulnerability was brought into different topics, the formalization of the construct was still fuzzy, thus articles did not benefit from this inclusion. Only after the year 2002, there have been the most prominent theoretical advancements that established the concept and provided useful frameworks [42] , [43] . However, topic generating articles are concentrated before the year 2000.

The number of authors , the type of article (review or non-review) and the size of literature are all positively and significantly associated with citations, and especially review articles have a large positive effect on citations. When we control for the position of the article in the literature, the author ' s experience and the size of literature lose significance, showing that, at least in this setting, the number of articles published in the field is not a good predictor for the success of the next article, nor is the size of the citing community. In our setting, there is no bandwagon effect or, if there is, it is not extended to all co-authors of the most cited papers. This property is more likely to reside in a handful of them (perhaps the first and last one) who can on the one hand produce outstanding articles, and on the other benefit from peer-recognition.

Discussion and Conclusion

In this work, we analyzed the effects on citations of the social and knowledge networks on which scientific articles are grounded. This work extends the knowledge accumulated on the effects of the co-authorship network on the scientific impact [2] , [3] , [7] , [15] , and tries to ground it better in the theory of knowledge combination upon which it is based [12] , [14] , [41] , [44] , by bringing in a second type of network that analyzes the knowledge sources. In particular, we felt compelled to answer two research questions: (1) how does the co-authorship network structure influence the scientific impact of an article in terms of citations? And (2) how do the article's knowledge sources influence its scientific impact? To answer these questions, we retrieve the articles published in the scientific literature on vulnerability of social and natural environment due to climate change and natural hazards. Then we construct the dynamics of the co-authorship and bibliographic coupling networks, based on co-authorship relations among authors and shared references among articles respectively.

With no surprise, we find that the structure of scientific collaborations matters. The cumulative number of co-authors has a positive – yet slight – impact on the citations of the article, while a larger and positive effect is given by the proximity of authors to all others in the field. However, the effect due to the proximity manifests itself only when it is possible to trace a collaboration link among a relevant share of authors in the field. This is consistent with the idea that knowledge diffusion is aided by personal relationships which could be costlessly and effortlessly tapped. Surprisingly, and in contrast to other studies [2] , [7] , we found that bridging among groups of authors is penalized in terms of citations. Such result is counterintuitive, because the intuition would suggest that authors who create a bridge among different groups, also bridge among their knowledge bases, thus their ideas could benefit from the eventual distance among knowledge domains. We believe that the intuition, and the theory, still hold, but we propose two reasons that could explain such a result. A first reason that could moderate the negative impact of bridging could be due to experience of the author who establishes ties with other groups, as studies in cognitive science claim [14] , [40] , [41] . The second reason is regards the measure of betweenness centrality, adopted in this and similar studies: betweenness centrality on the one hand overemphasizes the effect of the indirect connections and thus depends on the size of the component and on the other neglects the redundancy of edges among nodes. Therefore it does not reflect the number of diverse knowledge domains a single author can benefit from. Other measures could be explored in future research and a promising perspective could be given by an adaptation of the clustering coefficient computed over sets of nodes (the papers' co-authors), instead on individual ones. In other words, when two authors decide to work together, their respective networks get together, but they are still separated by the edge that links the two coauthors. Therefore, knowledge diversity must be computed counting the common ties between every possible dyad among all prior co-authors of the authors.

With regard to knowledge sources, we found that articles receive more citations when the authors are aware of what happens in different strands of literature, demonstrated by the references included in their article, and are able to make a synthesis between the preexisting and disconnected ideas. Thereby we claim that articles that find ways to tie together fragmented pieces of literature obtain more citations. Also we found that articles receive more citations when they are positioned in a literature that builds on a common base of articles.

Neither a specific benefit, nor a disadvantage comes from the size of the research community. We identified the size of the research community by looking at the practice adopted by authors who signal their belonging to a community by citing what ‘similar’ papers cite, thus boosting the degree centrality in the bibliographic coupling network.

Besides the use of the bibliographic coupling network, the adoption of topic modeling is a second innovative methodological component of this work. We used this tool to sort a large set of literature and control for innovative papers in terms of their content. We want to stress the utility of the tool in describing the findings – some unexpected – related to the topic variables. We saw that topic generating articles are concentrated in the early years (1989–2002) when the literature is still fragmented: the co-authorship network and bibliographic coupling network are made of many components. These are signs that the body of literature on vulnerability does neither cohesively grow upon seminal contributions nor as a unified body of literature whereby authors speak to each other. Instead, articles belong to different and separate domains (probably pertinent to the topics as pre-existing and different strands of literature) that introduce the concept of vulnerability in different years. We may conjecture that they do not benefit from importing the concept of vulnerability in their literature, perhaps because the concept is still fuzzy, differently defined in different domains, and seminal contributions had yet to come. As regard topic combination, the vast nature of the dataset allowed the identification of relatively distant topics such as drought, river basins and arctic, whose combination in the same article may signal a generic approach and wide scope rather than a provision of specific and innovative contributions. We believe that peer recognition comes from deep analyses and novel results: features that still elude our algorithms.

Indeed, we recognize that this work comes with limitations, which drive also ideas for future research. As already mentioned, the negative impact of the bridging author is yet unclear and future research should be carried out to discover why there seems to be no advantage to broker. We propose that future research may adopt team-adjusted measures of network constraints [45] , or clustering coefficient computed on the redundancies of the ties of sets of nodes.

We are aware that a second limitation is due to the large number of journals and edited books (1079 sources) that impeded us to control for possible journal effects on citations. We think that having a control for journals would absorb some of the variability in the data. However, such a limitation does not affect the validity of results that focused on the effect of co-authorship and bibliographic coupling network structures on the early citation received.

Another limitation comes with the simplification. We attributed the value given by the co-author with highest centrality scores to each article, and this prevented us from analyzing the information given by the heterogeneity of the set of co-authors. However, in this work, we think we have accomplished a first step to reconcile the often concealed knowledge aspect of generation of scientific knowledge with the more studied social one.

Supporting Information

Contains the files: Figure S1 – Scatterplot of the Bibliographic Coupling network data. Figure S2 – Scatterplot of the Co-authorship network data.


This work benefitted from the comments by Massimo Warglien on previous drafts of the paper and those of the participants at a seminar at the Laboratory of Experimental Economics at Ca' Foscari University of Venice, Venice, Italy.

Author Contributions

Conceived and designed the experiments: CB. Performed the experiments: CB. Analyzed the data: CB CG. Wrote the paper: CB. Designed the software used in the analysis: CB.

  • View Article
  • Google Scholar
  • 8. Newman MEJ (2009) The first-mover advantage in scientific publication. Epl 86..
  • 10. Catmull E (2008) How Pixar fosters collective creativity: Harvard Business School Publishing.
  • 12. Hargadon A, Sutton RI (1997) Technology brokering and innovation in a product development firm. Administrative Science Quarterly: 716–749.
  • 14. Simonton DK (1995) Foresight in insight? A Darwinian answer. In: Sternberg RJ, Davidson JE, editors. The nature of insight. Cambridge, MA: MIT Press. pp. 465–494.
  • 18. Sun XL, Kaur J, Milojevic S, Flammini A, Menczer F (2013) Social Dynamics of Science. Scientific Reports 3..
  • 22. Janssen MA (2007) An update on the scholarly networks on resilience, vulnerability, and adaptation within the human dimensions of global environmental change. Ecology and Society 12..
  • 28. Newman M (2010) Networks: An Introduction: OUP Oxford.
  • 30. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal, Complex Systems 1695.
  • 34. Kaplan S, Vakili K (2012) Identifying Breakthroughs: Cognitive vs. Economic.

ACM Digital Library home

  • Advanced Search

Sections-based bibliographic coupling for research paper recommendation

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options.

  • Sharma R Gopalani D Meena Y (2023) An anatomization of research paper recommender system Engineering Applications of Artificial Intelligence 10.1016/j.engappai.2022.105641 118 :C Online publication date: 1-Feb-2023
  • Mei X Cai X Xu S Li W Pan S Yang L (2022) Mutually reinforced network embedding Expert Systems with Applications: An International Journal 10.1016/j.eswa.2022.117616 204 :C Online publication date: 15-Oct-2022
  • Wang H Cheng J Yang C (2022) SentCite: a sentence-level citation recommender based on the salient similarity among multiple segments Scientometrics 10.1007/s11192-022-04339-0 127 :5 (2521-2546) Online publication date: 1-May-2022
  • Show More Cited By


A new bibliographic coupling measure with descriptive capability.

Bibliographic coupling (BC) is an effective measure to estimate the similarity between two scholarly articles (i.e., inter-article similarity between the two articles). It works on out-link references of articles (i.e., those references cited by the ...

The bibliographic coupling approach to filter the cited and uncited patent citations: a case of electric vehicle technology

Because some cited references are not relevant to the citing patent and not all the relevant references are cited, the study attempts to use the bibliographic coupling (BC) approach to filter the irrelevant patent citations and supplement the relevant ...

The dynamics of research subfields for library and information science: an investigation based on word bibliographic coupling

Uncovering research topics, manifesting the relationships, and revealing the structure in a discipline are major and important research issues in library and information science (LIS). To understand the evolution of research subfields in LIS ...


Published in.


Berlin, Heidelberg

Publication History

Author tags.

  • Bibliographic coupling
  • Citation proximity analysis
  • Logical sections
  • Paper recommendation


Other metrics, bibliometrics, article metrics.

  • 9 Total Citations View Citations
  • 0 Total Downloads
  • Downloads (Last 12 months) 0
  • Downloads (Last 6 weeks) 0
  • Zhang R Yuan J (2022) Enhanced author bibliographic coupling analysis using semantic and syntactic citation information Scientometrics 10.1007/s11192-022-04333-6 127 :12 (7681-7706) Online publication date: 1-Dec-2022
  • Ma B Zhang C Wang Y Deng S (2022) Enhancing identification of structure function of academic articles using contextual information Scientometrics 10.1007/s11192-021-04225-1 127 :2 (885-925) Online publication date: 1-Feb-2022
  • Cai X Wang N Yang L Mei X (2022) Global-local neighborhood based network representation for citation recommendation Applied Intelligence 10.1007/s10489-021-02964-5 52 :9 (10098-10115) Online publication date: 1-Jul-2022
  • Kreutz C Schenkel R (2022) Scientific paper recommendation systems: a literature review of recent publications International Journal on Digital Libraries 10.1007/s00799-022-00339-w 23 :4 (335-369) Online publication date: 1-Dec-2022
  • Hassan S Aljohani N Shabbir M Ali U Iqbal S Sarwar R Martínez-Cámara E Ventura S Herrera F (2020) Tweet Coupling: a social media methodology for clustering scientific publications Scientometrics 10.1007/s11192-020-03499-1 124 :2 (973-991) Online publication date: 1-Aug-2020
  • Ali Z Qi G Kefalas P Abro W Ali B (2020) A graph-based taxonomy of citation recommendation models Artificial Intelligence Review 10.1007/s10462-020-09819-4 53 :7 (5217-5260) Online publication date: 21-Feb-2020

View options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

  • Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
  • Download citation
  • Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again. no longer supports Internet Explorer.

To browse and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

  • We're Hiring!
  • Help Center

paper cover thumbnail

Paper recommendation using citation proximity in bibliographic coupling

Profile image of Dr. Raja Habib


Related Papers

PeerJ Computer Science

Hanan Aljuaid

From the past half of a century, identification of the relevant documents is deemed an active area of research due to the rapid increase of data on the web. The traditional models to retrieve relevant documents are based on bibliographic information such as Bibliographic coupling, Co-citations, and Direct citations. However, in the recent past, the scientific community has started to employ textual features to improve existing models’ accuracy. In our previous study, we found that analysis of citations at a deep level (i.e., content level) can play a paramount role in finding more relevant documents than surface level (i.e., just bibliography details). We found that cited and citing papers have a high degree of relevancy when in-text citations frequency of the cited paper is more than five times in the citing paper’s text. This paper is an extension of our previous study in terms of its evaluation of a comprehensive dataset. Moreover, the study results are also compared with other s...

research paper on bibliographic coupling

Joeran Beel

This paper presents an approach for identifying similar documents that can be used to assist scientists in finding related work. The approach called Citation Proximity Analysis (CPA) is a further development of co-citation analysis, but in addition, considers the proximity of citations to each other within an article’s full-text. The underlying idea is that the closer citations are to each other, the more likely it is that they are related. In comparison to existing approaches, such as bibliographic coupling, co-citation analysis or keyword based approaches the advantages of CPA are a higher precision and the possibility to identify related sections within documents. Moreover, CPA allows a more precise automatic document classification. CPA is used as the primary approach to analyse the similarity and to classify the 1.2 million publications contained in the research paper recommender system

Computational Science and Its Applications – ICCSA 2018

Sutrisna Wibawa

Intelligent Information Management

Mohsen Kahani

2019 IEEE International Conference on Big Data (Big Data)

Magessa Mgambwa

With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques.

The relatedness of research articles, patents, legal documents, web pages, and other documents is often calculated with citation or hyperlink based approaches such as citation proximity analysis (CPA). In contrast to text-based document similarity, citation-based relatedness covers a broader range of relatedness. However, citation-based approaches suffer from the many documents that receive little or no citations, and for which document relatedness hence cannot be calculated. I propose to calculate a machine-learned 'virtual citation proximity' (or 'virtual hyperlink proximity') that could be calculated for all documents for which textual information (title, abstract …) and metadata (authors, journal name …) is available. The input to the machine learning algorithm would be a large corpus of documents, for which textual information, metadata and citation proximity is available. The citation proximity would serve as ground truth, and the machine-learning algorithm would infer, which textual features correspond to a high proximity of co-citations. After the training phase, the machine-learning algorithm could calculate a virtual citation proximity even for uncited documents. This virtual citation proximity would express in what proximity two documents would likely be cited, if they were cited. The virtual citation proximity then could be used in the same way as "real" citation proximity to calculate document relatedness, and would potentially cover a wider range of relatedness than text-based document relatedness.

2016 11th Iberian Conference on Information Systems and Technologies (CISTI)

sokiato gulo

IEEE Access

Khalid Haruna

Tamara Heck

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.


Proceedings of the …

Syed Khadimullah

Teshome Bekele

Library Hi Tech

Andre Vellino


Peer-to-Peer Networking and Applications

Mohammed Alhamid

Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries - JCDL '13

Min-Yen Kan

IEEE/ACM Joint Conference on Digital Libraries

Wenyi Huang

International Conference on Emerging …

Ashish Patel

Information Storage and Retrieval

Bella Weinberg

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Daniel Duma

Proceedings of the 19th international conference on World wide web - WWW '10

Prasenjit Mitra

2012 International Conference on Collaboration Technologies and Systems (CTS)

Decision Support Systems

Journal of the American society for information …

Henry Small

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Dwaipayan Roy

International Journal of Advanced Computer Science and Applications

Simon Philip

Gloria Gheno

Information Retrieval

Marcos Goncalves

IEEE Data(base) Engineering Bulletin

Sulieman Ahmad


  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024

Inventor bibliographic-patent-coupling analysis and inventor-patent-classification-coupling analysis: a comparative analysis based on NPE

  • Published: 05 August 2023
  • Volume 129 , pages 745–765, ( 2024 )

Cite this article

research paper on bibliographic coupling

  • Song Yanhui   ORCID: 1 &
  • Lei Lixin 1  

491 Accesses

Explore all metrics

The patent literature is an important scientific and technological literature, which integrates technical information, market information, and legal information. It is of great significance to expand the bibliometric methods to the measurement of patent literature. This paper takes 4624 NPE (Non Practicing Entities) patents as samples and establishes an inventor coupling network based on two types of feature items, patent literature and Derwent classification codes. We have explored the technical structure of NPE patents. Through centrality analysis, correlation analysis, factor analysis, and visualization analysis, the two coupling analysis methods of Inventor Bibliographic-Patent-Coupling (IBPCA) and Inventor Patent Classification-Coupling (IPPCA) are compared. It is found that inventor centrality analysis, frequency correlation analysis, and cosine similarity measurement all show that IBPCA is correlated with IPPCA; The core technical topics of NPE patents discovered by IBPCA and IPCCA are digital computers, digital telecommunication transmission, and data storage and transmission. However, the two methods differ in factor models fitting analysis and intellectual structure detection. The factor fitting analysis of IPPCA is better than that of IBCCA; IBPCA can detect more topics than IPCCA, and has more advantages in small-scale topic detection; IPCCA is more sensitive to traditional and more stable research topics. Therefore, The combination of the two methods for intellectual structure detection and analysis will be more effective, then more comprehensive and specific conclusions will be obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

research paper on bibliographic coupling

Similar content being viewed by others

Using the comprehensive patent citation network (cpc) to evaluate patent value, evolution monitoring for innovation sources using patent cluster analysis.

research paper on bibliographic coupling

Recent Advances in Patent Analysis Network

Abbas, A., Zhang, L., & Khan, S. U. (2014). A literature review on the state-of-the-art in patent analysis. World Patent Information, 37 , 3–13.

Article   Google Scholar  

Barirani, A., Agard, B., & Beaudry, C. (2013). Discovering and assessing fields of expertise in nanomedicine: A patent co-citation network perspective. Scientometrics, 94 (3), 1111–1136.

Bonino, D., Ciaramella, A., & Corno, F. (2010). Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics. World Patent Information, 32 (1), 30–38.

Article   CAS   Google Scholar  

Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61 (12), 2389–2404.

Chang, S. H., & Fan, C. Y. (2016). Identification of the technology life cycle of telematics: A patent-based analytical perspective. Technological Forecasting and Social Change, 105 , 1–10.

Chang, Y. W., Huang, M. H., & Lin, C. W. (2015). Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses. Scientometrics, 105 (3), 2071–2087.

Chen, S. H., Huang, M. H., Chen, D. Z., & Lin, S. Z. (2012). Detecting the temporal gaps of technology fronts: A case study of smart grid field. Technological Forecasting and Social Change, 79 (9), 1705–1719.

Chen, Y., & Fang, S. (2011). Methods of social network analysis on patent assignees’ correlation networks. Documentation, Information & Knowledge, 3 , 58–66.

Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73 (8), 981–1012.

Ferreira, F. A. F. (2018). Mapping the field of arts-based management: Bibliographic coupling and co-citation analyses. Journal of Business Research, 85 , 348–357.

Gmür, M. (2003). Co-citation analysis and the search for invisible colleges: A methodological evaluation. Scientometrics, 57 (1), 27–57.

Hasner, C., de Lima, A. A., & Winter, E. (2019). Technology advances in sugarcane propagation: A patent citation study. World Patent Information, 56 (9), 16.

Hou, J., Yang, X., & Chen, C. (2018). Emerging trends and new developments in information science: A document co-citation analysis (2009–2016). Scientometrics, 115 (2), 869–892.

Hsiao, T. M., & Chen, K. H. (2020). The dynamics of research subfields for library and information science: An investigation based on word bibliographic coupling. Scientometrics, 125 (1), 717–737.

Huang, M. H., & Chang, C. P. (2014). A comparative study on detecting research fronts in the organic light-emitting diode (OLED) field using bibliographic coupling and co-citation. Scientometrics, 102 (3), 2041–2057.

Huang, M. H., Chiang, L. Y., & Chen, D. Z. (2003a). Constructing a patent citation map using bibliographic coupling: A study of Taiwan’s high-tech companies. Scientometrics, 58 (3), 489–506.

Huang, Z., Chen, H., Yip, A., Ng, G., Guo, F., Chen, Z. K., & Roco, M. C. (2003b). Longitudinal patent analysis for nanoscale science and engineering: country, institution and technology field. Journal of Nanoparticle Research, 5 (3), 333–363.

Jiang, J., Shi, P., An, B., Yu, J., & Wang, C. (2017). Measuring the social influences of scientist groups based on multiple types of collaboration relations. Information Processing & Management, 53 (1), 1–20.

Jun, S., Sung Park, S., & Sik Jang, D. (2012). Technology forecasting using matrix map and patent clustering. Industrial Management & Data Systems, 112 (5), 786–807.

Kang, I. S., Na, S. H., Kim, J., & Lee, J. H. (2007). Cluster-based patent retrieval. Information Processing & Management, 43 (5), 1173–1182.

Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14 (1), 10–25.

Kim, G., & Bae, J. (2017). A novel approach to forecast promising technology through patent analysis. Technological Forecasting and Social Change, 117 , 228–237.

Kuan, C. H., Chen, D. Z., & Huang, M. H. (2019). Bibliographically coupled patents: Their temporal pattern and combined relevance. Journal of Informetrics .

Kuan, C. H., Huang, M. H., & Chen, D. Z. (2018). Missing links: Timing characteristics and their implications for capturing contemporaneous technological developments. Journal of Informetrics, 12 (1), 259–270.

Kuusi, O., & Meyer, M. (2007). Anticipating technological breakthroughs: Using bibliographic coupling to explore the nanotubes paradigm. Scientometrics, 70 (3), 759–777.

Lai, K. K., & Wu, S. J. (2005). Using the patent co-citation approach to establish a new patent classification system. Information Processing & Management, 41 (2), 313–330.

Lee, K., & Lee, J. (2020). National innovation systems, economic complexity, and economic growth: Country panel analysis using the US patent data. Journal of Evolutionary Economics, 30 (4), 897–928.

Leydesdorff, L., Kushnir, D., & Rafols, I. (2012). Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC). Scientometrics, 98 (3), 1583–1599.

Liu, W., Nanetti, A., & Cheong, S. A. (2017). Knowledge evolution in physics research: An analysis of bibliographic coupling networks. PLoS ONE .

Article   PubMed   PubMed Central   Google Scholar  

Lo, S. C. (2007). Patent coupling analysis of primary organizations in genetic engineering research. Scientometrics, 74 (1), 143–151.

McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41 (6), 433–443.;2-Q

Narin, F. (1994). Patent bibliometrics. Scientometrics, 30 (1), 147–155.

Nerur, S. P., Rasheed, A. A., & Natarajan, V. (2008). The intellectual structure of the strategic management field: An author co-citation analysis. Strategic Management Journal, 29 (3), 319–336.

Noh, H., Jo, Y., & Lee, S. (2015). Keyword selection and processing strategy for applying text mining to patent analysis. Expert Systems with Applications, 42 (9), 4348–4360.

Noh, H., Song, Y. K., & Lee, S. (2016). Identifying emerging core technologies for the future: Case study of patents published by leading telecommunication organizations. Telecommunications Policy, 40 (10), 956–970.

Park, A., Conway, M., & Chen, A. T. (2018a). Examining thematic similarity, difference, and membership in three online mental health communities from reddit: A text mining and visualization approach. Computers in Human Behavior, 78 , 98–112.

Article   PubMed   Google Scholar  

Park, I., Jeong, Y., Yoon, B., & Mortara, L. (2014). Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis. Technology Analysis & Strategic Management, 27 (7), 759–781.

Park, I., Jeong, Y., Yoon, B., & Mortara, L. (2015). Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis. Technology Analysis & Strategic Management, 27 (7), 759–781.

Park, T. Y., Lim, H., & Ji, I. (2018b). Identifying potential users of technology for technology transfer using patent citation analysis: A case analysis of a Korean research institute. Scientometrics, 116 (3), 1541–1558.

Pénin, J. (2012). Strategic uses of patents in markets for technology: A story of fabless firms, brokers and trolls. Journal of Economic Behavior & Organization, 84 (2), 633–641.

Rodriguez, A., Kim, B., Turkoz, M., Lee, J. M., Coh, B. Y., & Jeong, M. K. (2015). New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network. Scientometrics, 103 (2), 565–581.

Shen, J., Gao, J., & Teng, L. (2012). Derwent manual code co-occurrence: A practical method in patent map. Science of Science and Management of S & T, 33 (1), 12–16.

Google Scholar  

Small, H. (1973). Co-citation in the scientific literature a new measure of the relationship between two documents. Journal of the American Society for Information Science, 24 (24), 265–269.

Small, H., & Griffith, B. C. (1974). The structure of scientific literatures I: Identifying and graphing specialties. Science Studies, 4 (1), 17–40.

Small, H. G., & Koenig, M. E. D. (1977). Journal clustering using a bibliographic coupling method. Information Processing & Management, 13 (5), 277–288.

Song, Y., & Wu, Y. (2014). A comparative study on author bibliographic-coupling analysis and author keyword-coupling analysis based on scientometrics. The Journal of the Library Science in China, 40 (1), 25–38.

Swanson, D. R. (1971). Some unexplained aspects of the cranfield tests of indexing performance factors. The Library Quarterly, 41 (3), 223–228.

Tseng, Y. H., Lin, C. J., & Lin, Y. I. (2007). Text mining techniques for patent analysis. Information Processing & Management, 43 (5), 1216–1247.

Von Wartburg, I., Teichert, T., & Rost, K. (2005). Inventive progress measured by multi-stage patent citation analysis. Research Policy, 34 (10), 1591–1607.

Wang, J., & Hsu, C. C. (2020). A topic-based patent analytics approach for exploring technological trends in smart manufacturing. Journal of Manufacturing Technology Management, 32 (1), 110–135.

Wen, F. (2017). Research on the technology diversity and similarity based on the coupling of derwent patent classification codes. Information Studies: Theory & Application, 40 (8), 87–92.

White, H. D. (2003). Author cocitation analysis and Pearson’s r. Journal of the American Society for Information Science and Technology, 54 (13), 1250–1259.

White, H. D., & Griffith, B. C. (1981). Author cocitation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32 (3), 163–171.

Yang, S., Han, R., Wolfram, D., & Zhao, Y. (2016). Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis. Journal of Informetrics, 10 (1), 132–150.

Yeh, H. Y., Sung, Y. S., Yang, H. W., Tsai, W. C., & Chen, D. Z. (2012). The bibliographic coupling approach to filter the cited and uncited patent citations: A case of electric vehicle technology. Scientometrics, 94 (1), 75–93.

Yoon, B., & Magee, C. L. (2018). Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction. Technological Forecasting and Social Change, 132 , 105–117.

Zhang, K., Xia, W., Yuan, J., Chen, J., & Geng, Y. (2015). Study on the definition, types and characteristics of NPEs. Science and Technology Management Research, 35 (15), 141–146+151.

Zhang, Y., Shang, L., Huang, L., Porter, A. L., Zhang, G., Lu, J., & Zhu, D. (2016). A hybrid similarity measure method for patent portfolio analysis. Journal of Informetrics, 10 (4), 1108–1130.

Zhao, D., & Strotmann, A. (2008a). Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic-coupling analysis. Journal of the American Society for Information Science and Technology, 59 (13), 2070–2086.

Zhao, D., & Strotmann, A. (2008b). Information science during the first decade of the web: An enriched author cocitation analysis. Journal of the American Society for Information Science and Technology, 59 (6), 916–937.

Zhao, D., & Strotmann, A. (2014). The knowledge base and research front of information science 2006–2010: An author cocitation and bibliographic coupling analysis. Journal of the Association for Information Science and Technology, 65 (5), 995–1006.

Zitt, M., & Bassecoulard, E. (2006). Delineating complex scientific fields by an hybrid lexical-citation method: An application to nanosciences. Information Processing & Management, 42 (6), 1513–1531.

Download references


This study was funded in part by major project of National Social Science Foundation of China (19ZDA348), and Supported by the Fundamental Research Funds for the Provincial Universities of Zhejiang (GK209907299001-201).

Major project of National Social Science Foundation of China, 19ZDA348, Song Yanhui,Fundamental Research Funds for the Provincial Universities of Zhejiang, GK209907299001-201, Song Yanhui.

Author information

Authors and affiliations.

School of Management, Hangzhou Dianzi University, Hangzhou, 310018, China

Song Yanhui & Lei Lixin

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Song Yanhui .

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Yanhui, S., Lixin, L. Inventor bibliographic-patent-coupling analysis and inventor-patent-classification-coupling analysis: a comparative analysis based on NPE. Scientometrics 129 , 745–765 (2024).

Download citation

Received : 22 March 2021

Accepted : 19 April 2023

Published : 05 August 2023

Issue Date : February 2024


Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Inventor bibliographic-patent-coupling analysis
  • Inventor patent classification-coupling analysis
  • Patent analysis
  • Find a journal
  • Publish with us
  • Track your research

Help | Advanced Search

Condensed Matter > Mesoscale and Nanoscale Physics

Title: strong charge-photon coupling in planar germanium enabled by granular aluminium superinductors.

Abstract: High kinetic inductance superconductors are gaining increasing interest for the realisation of qubits, amplifiers and detectors. Moreover, thanks to their high impedance, quantum buses made of such materials enable large zero-point fluctuations of the voltage, boosting the coupling rates to spin and charge qubits. However, fully exploiting the potential of disordered or granular superconductors is challenging, as their inductance and, therefore, impedance at high values are difficult to control. Here we have integrated a granular aluminium resonator, having a characteristic impedance exceeding the resistance quantum, with a germanium double quantum dot and demonstrate strong charge-photon coupling with a rate of $g_\text{c}/2\pi= (566 \pm 2)$ MHz. This was achieved due to the realisation of a wireless ohmmeter, which allows \emph{in situ} measurements during film deposition and, therefore, control of the kinetic inductance of granular aluminium films. Reproducible fabrication of circuits with impedances (inductances) exceeding 13 k$\Omega$ (1 nH per square) is now possible. This broadly applicable method opens the path for novel qubits and high-fidelity, long-distance two-qubit gates.
Subjects: Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Quantum Physics (quant-ph)
Cite as: [cond-mat.mes-hall]
  (or [cond-mat.mes-hall] for this version)
  Focus to learn more arXiv-issued DOI via DataCite

Submission history

Access paper:.

  • HTML (experimental)
  • Other Formats

license icon

References & Citations

  • Google Scholar
  • Semantic Scholar

BibTeX formatted citation

BibSonomy logo

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

  • Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .


  1. Example of a bibliographic coupling between two articles. Source: the

    research paper on bibliographic coupling

  2. Bibliographic coupling of articles.

    research paper on bibliographic coupling

  3. Bibliographic Coupling of documents

    research paper on bibliographic coupling

  4. Bibliographic coupling: a main path analysis from 1963 to 2020

    research paper on bibliographic coupling

  5. Bibliographic coupling of documents

    research paper on bibliographic coupling

  6. Example of a bibliographic coupling between two articles. Source: the

    research paper on bibliographic coupling


  1. CSC 641, Fall 2020: 1.10 Bibliographic coupling

  2. BDDC Coupling Mechanism

  3. Lecture 16: Relationship between Coupling and Cohesion

  4. Bibliographic Coupling

  5. How to create citation/bibliographic lists for your research and literature review

  6. 44. Coupling coefficient: Example 13.3


  1. Bibliographic coupling and its application to research-front and other

    In a subsequent report dated 1962, Kessler applied bibliographic coupling to a test population of 40 documents from the field of radio engineering in order to test if a number of scientific documents bear meaningful relations to one another. He found that bibliographic coupling was able to partition this population into valid, related sub-groups.

  2. Enhanced author bibliographic coupling analysis using semantic and

    The bibliographic coupling (BC) concept originated from Kessler's discovery: when two documents are more similar in topic, they share more common references.Zhao and Strotmann extended BC and proposed author bibliographic coupling analysis (ABCA).ABCA excels at characterizing the research interest of communities of current authors and revealing a field's research front and realistic ...

  3. How to conduct a bibliometric analysis: An overview and guidelines

    Bibliographic coupling is a technique for science mapping that operates on the assumption that two publications sharing common references are also ... or low hundreds (e.g., 100-300) of papers, then the research field is considered to be small and thus do not warrant the use of bibliometric analysis, as forcing the analysis on this small ...

  4. Bibliographic coupling networks reveal the advantage of diversification

    The larger the GC ratio, the more research papers in the core field that year. In order to have a better distinction between different items, we set threshold to 5 (two papers cited at least five of the same articles) to establish bibliographic coupling networks and identify their giant components, which is the same as the method in Fig. 4-b.

  5. Author bibliographic coupling: Another approach to citation‐based

    Both these limitations may, theoretically, be addressed through extending the concept of bibliographic coupling (BC) from the document level to an author-aggregated (i.e., oeuvre) approach. The BC concept was introduced a decade earlier (Kessler, 1963) than that of co-citation (Small, 1973) as a way to cluster research papers. The BC strength ...

  6. Co‐citation analysis, bibliographic coupling, and direct citation

    In this study we compare the accuracies of cluster solutions of a large corpus of 2,153,769 recent articles from the biomedical literature (2004-2008) using four similarity approaches: co-citation analysis, bibliographic coupling, direct citation, and a bibliographic coupling-based citation-text hybrid approach.

  7. Sections-based bibliographic coupling for research paper ...

    Researchers have proposed numerous approaches for research paper recommendation which are based on metadata, content, citation analysis, collaborative filtering, etc. Approaches based on citation analysis, including co-citation and bibliographic coupling, have proven to be significant. Researchers have extended the co-citation approach to ...

  8. Author bibliographic coupling: Another approach to citation‐based

    While bibliographic coupling (BC) as a measure of relatedness between documents was proposed a full decade before co-citation, interest in applying BC to mapping the intellectual structure of research areas has only recently resurged, perhaps because it allows researchers to circumvent problems of the so far dominant co-citation analysis.

  9. Co-Authorship and Bibliographic Coupling Network Effects on ...

    This paper analyzes the effects of the co-authorship and bibliographic coupling networks on the citations received by scientific articles. It expands prior research that limited its focus on the position of co-authors and incorporates the effects of the use of knowledge sources within articles: references. By creating a network on the basis of shared references, we propose a way to understand ...

  10. PDF Sections-based bibliographic coupling for research paper ...

    Researchers have proposed numer-ous approaches for research paper recommendation which are based on metadata, content, citation analysis, collaborative filtering, etc. Approaches based on citation analysis, includ-ing co-citation and bibliographic coupling, have proven to be significant. Researchers have extended the co-citation approach to ...

  11. Sections-based bibliographic coupling for research paper recommendation

    Researchers have proposed numerous approaches for research paper recommendation which are based on metadata, content, citation analysis, collaborative filtering, etc. Approaches based on citation analysis, including co-citation and bibliographic coupling, have proven to be significant. Researchers have extended the co-citation approach to ...

  12. Bibliographic coupling

    Bibliographic coupling, like co-citation, is a similarity measure that uses citation analysis to establish a similarity relationship between documents. ... The paper is more a memoir than a research paper, filled with decisions, research expectations, interests and motivations—including the story of how Henry Small approached Belver Griffith ...

  13. (PDF) Bibliographic coupling: A review

    T h e classic paper on bibliographic coupling [13] appeared in American Documentation that same year. A computer program was written to process the citations of 36 volumes of the Physical Review, a total of 137,000 references. ... He uses the method to establish a classification of papers found in the research front. Using a sample of papers in ...

  14. Generalization of bibliographic coupling and co-citation using the node

    1. Introduction. With the long history of science studies, the investigation of citation-based similarity has been a major topic of research for information scientists (Kessler, 1963a, Kessler, 1963b, Marshakova, 1973, Price, 2011, Small, 1973).Although direct citation (DC) is the clearest and simplest measure of similarity, the linkage may be incomplete because of its innate limitations.

  15. Sections-based bibliographic coupling for research paper recommendation

    Sections-based bibliographic coupling for research paper recommendation. March 2019. Scientometrics 119 (9) DOI: 10.1007/s11192-019-03053-8. Authors: Raja Habib. Muhammad Tanvir Afzal. Shifa ...

  16. Bibliographic coupling Research Papers

    The term 'core document' denote documents that have a central position in the research front in terms of many and strong bibliographic coupling links. The identification and mapping of core documents usually requires a large multidisciplinary research setting and in this study the 2003 volume of the Science Citation Index was applied.

  17. Paper recommendation using citation proximity in bibliographic coupling

    6. Conclusion Research paper recommender systems are becoming increasingly important for researchers due to the information overload. In this paper, we introduced a bibliographic coupling-based paper recommendation system that uses in-text citation proximity. We used the DBSCAN algorithm to cluster the in-text citations.

  18. PDF A new methodological approach to bibliographic coupling and its

    research-front or core documents which represent recent 'hot' and other research-front topics. Besides the methodology only global journal, subfield and country ... The strength of bibliographic coupling of two papers can thus be visualized as the angle between the corresponding Boolean vectors. This may help to find appropriate thresholds ...


    graphic coupling should be theoretically elaborated and a general mathematical framework be evolved. Sharada and Sharma [7] recently carried out a study of bibliographic coupling in linguistic research by comparing the research papers published in an Indian journal vis-a-vis an American journal. In the present paper, an attempt is made to study the

  20. Ferromagnetic inter-layer coupling in FeSe$_{1-x}$S$_{x

    Here, using inelastic neutron scattering to study spin excitations in single-crystal samples, we reveal that the magnetic coupling between adjacent Fe layers is not only significant, as it affects excitations up to \textcolor{black}{15} meV, but also ferromagnetic in nature, making the system different from most unconventional superconductors ...

  21. [2407.05195] Subgap-state-mediated transport in superconductor

    Superconductor-semiconductor hybrid systems play a crucial role in realizing nanoscale quantum devices, including hybrid qubits, Majorana bound states, and Kitaev chains. For such hybrid devices, subgap states play a prominent role in their operation. In this work, we study such subgap states via Coulomb and tunneling spectroscopy through a superconducting island defined in a semiconductor ...

  22. Bibliographic coupling: A review

    Further, bibliographic coupling analysis presents six emerging themes in MC research, such as MC conceptualization, responsible consumption, and responsible practices by firms. Finally, the paper proposes future avenues for research for the resultant clusters. Risk analysis of onshore oil and gas pipelines: Literature review and bibliometric ...

  23. [2407.03293] Microscopic theory for electron-phonon coupling in twisted

    The origin of superconductivity in twisted bilayer graphene -- whether phonon-driven or electron-driven -- remains unresolved. The answer to this question is hindered by the absence of a quantitative and efficient model for electron-phonon coupling (EPC). In this work, we develop a first-principles-based microscopic theory to calculate EPC in twisted bilayer graphene for arbitrary twist angles ...

  24. A new methodological approach to bibliographic coupling and its

    In an earlier study the authors have shown that bibliographic coupling techniques can be used to identify 'hot' research topics. The methodology is based on appropriate thresholds for both number of related documentsand the strength of bibliographic links. Those papers are calledcore documents that have more than 9 links of at least the strength 0.25 according toSalton's measure, provided ...

  25. Bibliographic Coupling

    In the language of set theory bibliographic coupling, bibliographic coupling strength and relative bibliographic coupling strength are defined as follows. Let X and Y be two documents. If R(X) denotes the set of papers in the reference list of document X and R(Y) the set of papers in the reference list of Y then R(X)∩R(Y), the intersection of R(X) and R(Y), is the set of papers belonging to ...

  26. Inventor bibliographic-patent-coupling analysis and inventor ...

    The author bibliographic coupling promotes the bibliographic coupling analysis to the author level, not just stay in the paper level, with the author of the paper as the main research object (Zhao & Strotmann, 2014). After expanding to the author level, the bibliographic coupling overcomes the original shortcoming of fixed quantity and becomes ...

  27. [2407.04062] Single-mode emission by phase-delayed coupling between

    Near-field coupling between nanolasers enables collective high-power lasing but leads to complex spectral reshaping and multimode operation, limiting the emission brightness, spatial coherence and temporal stability. Many lasing architectures have been proposed to circumvent this limitation, based on symmetries, topology, or interference. We show that a much simpler and robust method ...

  28. [2407.02024] Parametric Light-Matter Interaction in the Single-Photon

    Parametric coupling between harmonic oscillators has enabled exquisite measurement precision and control of linear resonators, being extensively studied, for example, in cavity optomechanics. This level of control has been made possible by using strong sideband drives, enhancing the coupling rate while also linearizing the interaction. In this article, we demonstrate a new paradigm of ...

  29. [2407.04691] Exceptional Points and Braiding Topology in Non-Hermitian

    View a PDF of the paper titled Exceptional Points and Braiding Topology in Non-Hermitian Systems with long-range coupling, by S. M. Rafi-Ul-Islam and 3 other authors ... (PBC), which can be controlled by varying the coupling strengths. A topological invariant, namely the braiding index, is introduced to characterize the different complex energy ...

  30. [2407.03079] Strong Charge-Photon Coupling in Planar Germanium Enabled

    View a PDF of the paper titled Strong Charge-Photon Coupling in Planar Germanium Enabled by Granular Aluminium Superinductors, by Mari\'an Jan\'ik and 13 other authors View PDF HTML (experimental) Abstract: High kinetic inductance superconductors are gaining increasing interest for the realisation of qubits, amplifiers and detectors.