Predicting Researchers' Future Activities Using Visualization System for Co-authorship Networks

Studies of bibliographic data suggest a strong correlation between the growth of citation networks and their corresponding co-authorship networks. We explore the interdependence between evolving citation and co-authorship networks focused on the publications, by Indian authors, in American Physical Society journals between 1970 and 2013. We record interactions between each possible pair of authors in two ways: first, by tracing the change in citations they exchanged and, second, by tracing the shortest path between authors in the co-authorship network. We create these data for every year of the period of our analysis. We use probability methods to quantify the correlation between citations and shortest paths, and the effect on the dynamics of the citation-co-authorship system. We find that author pairs who have a co-authorship distance $d \leq 3$ significantly affect each others citations, but that this effect falls off rapidly for longer distances in the co-authorship network. The exchange of citation between pairs with $d=1$ exhibits a sudden increase at the time of first co-authorship events and decays thereafter, indicating an aging effect in collaboration. This suggests that the dynamics of the co-authorship network appear to be driving those of the citation network rather than vice versa. Moreover, the majority of citations received by most authors are due to reciprocal citations from current, or past, co-authors. We conclude that, in order to answer questions on nature and dynamics of scientific collaboration, it is necessary to study both co-authorship and citation network simultaneously.

BackgroundThe 2004 Mexico Declaration, and subsequent World Health Assembly resolutions, proposed a concerted support for the global development of health policy and systems research (HPSR). This included coordination across partners and advocates for the field of HPSR to monitor the development of the field, while promoting decision-making power and implementing responsibilities in low- and middle-income countries (LMICs).MethodsWe used a network science approach to examine the structural properties of the HPSR co-authorship network across country economic groups in the PubMed citation database from 1990 to 2015. This analysis summarises the evolution of the publication, co-authorship and citation networks within HPSR.ResultsThis method allows identification of several features otherwise not apparent. The co-authorship network has evolved steadily from 1990 to 2015 in terms of number of publications, but more importantly, in terms of co-authorship network connectedness. Our analysis suggests that, despite growth in the contribution from low-income countries to HPSR literature, co-authorship remains highly localised. Lower middle-income countries have made progress toward global connectivity through diversified collaboration with various institutions and regions. Global connectivity of the upper middle-income countries (UpperMICs) are almost on par with high-income countries (HICs), indicating the transition of this group of countries toward becoming major contributors to the field.ConclusionsNetwork analysis allows examination of the connectedness among the HSPR community. Initially (early 1990s), research groups operated almost exclusively independently and, despite the topic being specifically on health policy in LMICs, HICs provided lead authorship. Since the early 1990s, the network has evolved significantly. In the full set analysis (1990–2015), for the first time in HPSR history, more than half of the authors are connected and lead authorship from UpperMICs is on par with that of HICs. This demonstrates the shift in participation and influence toward regions which HPSR primarily serves. Understanding these interactions can highlight the current strengths and future opportunities for identifying new strategies to enhance collaboration and support capacity-building efforts for HPSR.

In the context of scientometrics and bibliometrics, several research fields are dealing with bibliographic data. In this paper, we will explore how the combination of online analytical processing (OLAP) analysis and information networks could be an interesting issue. In Business Intelligence, OLAP is a technology supported by data warehousing systems. It provides tools for analyzing data according to multiple dimensions and multiple hierarchical levels. At the same time, several information networks (co-authors network, citations network, institutions network, etc.) can be built based on bibliographic databases. Originally, OLAP was introduced to analyze structured data. However, in this paper, we wonder if, by combining OLAP and information networks, we can provide a new way of analyzing bibliographic data. OLAP should be able to handle information networks and be also useful for monitoring, browsing and analyzing the content and the structure of bibliographic networks. The goal of this survey paper is to review previous work on OLAP and information networks dealing with bibliographic data. We also propose a comparison between traditional OLAP and OLAP on information networks and discuss the challenges OLAP faces regarding bibliographic networks.

This thesis examines research collaboration patterns and knowledge structures in the interdisciplinary field of renewable energy and biodiversity. As global energy transitions accelerate, understanding how researchers collaborate and share knowledge across disciplinary boundaries becomes increasingly important. Employing scientometric methods and social network analysis, this study provides a comprehensive structural analysis of the field. The analysis is based on 936 academic papers sourced from the OpenAlex database, from which four distinct networks were constructed: author co-authorship, institution co-authorship, author co-citation, and institution co-citation networks. Following rigorous data cleaning and disambiguation procedures, the study computes key network metrics including degree centrality, strength, betweenness centrality, clustering coefficient, and average path length. Community detection algorithms and small-world coefficient tests are applied to uncover underlying structural patterns. Results reveal that both collaboration and citation networks exhibit distinct core-periphery structures. Author networks demonstrate small-world characteristics, indicating efficient information flow despite local clustering. The analysis identifies pivotal scholars, influential institutions, and dominant research communities within the field. Notably, the study reveals a divergence between collaborative activity and academic influence, suggesting that different mechanisms drive social and cognitive structures. These findings contribute to understanding knowledge production dynamics in interdisciplinary research and offer practical insights for research policy and cross-disciplinary collaboration. Keywords: research collaboration, co-authorship network, co-citation network, social network analysis, renewable energy, biodiversity

• In economics journals, the share of theoretical work declined between 2000 and 2024, while empirical research, especially in development, labor, and public economics, expanded and received substantially higher citation counts. • Network analysis of economics journals reveals persistent concentrations of scholarly influence. • In the economics discipline, a small set of elite U.S. universities occupies central positions in citation and co-authorship networks. • Despite changes in research topics and methods, the structure of scholarly knowledge production in economics remains hierarchical, suggesting that intellectual innovation occurs within a stable institutional core. This paper examines patterns in knowledge production within the top-five economics journals from 2000 to 2024. Drawing on the full set of published articles and associated bibliometric details, citations, co-authorship links, and institutional affiliations, I document notable shifts in the orientation of research. The share of theoretical work has declined, while empirical fields, particularly development, labor, and public economics, have expanded. Network analysis shows that although the substantive questions and analytical approaches in top-five journals are changing, a small group of U.S. universities continues to account for a disproportionate share of highly cited work and occupies central positions in citation networks, a core that at the same time facilitates communication and exchange across fields.

Research activities and collaborations in nanoscale science and engineering have major implications for advancing technological frontiers in many fields including medicine, electronics, energy, and...

To date, many studies of scientific citation, collaboration and coauthorship networks have focused on the concept of cross-community ties. In this article we explore how Semantometrics can help to characterise the types of research collaboration in scholarly publication networks and the nature of the cross-community ties, and how this information can be utilised in aiding research evaluation. In contrast to the existing research evaluation metrics such as Bibliometrics, Altmetrics or Webometrics, which are based on measuring the number of interactions in the scholarly network, Semantometrics build on the premise that fulltext is needed to understand the value of publications. Using the CORE dataset as a case study, this paper looks at the relation between the semantic distance of authors and their research endogamy value. We identify four potential types of collaboration in a coauthorship network. The results suggest similar measures can be used to provide meaningful information about the nature of collaboration in scholarly publication networks.

Networks of citations among the papers on a research topic reflect the structure of scholarship on that topic. Reference-network data for research areas from several disciplines show substantial variation in the structure of scholarship, ranging from the frequent and disproportionate citation of recent work to the frequent and disproportionate citation of foundational documents. The variation is inconsistent with the pattern expected of a simple physical sciences-behavioral sciences-humanities dimension. Citation-context analyses of references in the networks of various fields suggest that variation in network structure is due in part to differences in why authors cite their colleagues' work: Disproportionate citation of foundational documents occurs when authors cite papers as examples of perspectives or general approaches rather than as support for specific points. Differences in use patterns for citations can help us understand other differences among scholarly communities

The advent of proliferation of social networking is high on use in present era. A co-authorship network which shows research collaborations, are an important class of social networks. Research collaborations often yield good results but organizing a research group is a tedious task. Every researcher is concerned to collaborate with the best expertise complimenting him. Although there was abundant research conducted to find future collaborators or links, very few of them are able to find out effective relationship among them. In this article, we propose a method that makes link predictions in co-authorship networks using supervised approach. The model extracts the features from the networks node and topological structure which can be good indicators of future collaborations. The proposed method was evaluated on synthetic as well as real social networks such as Net Science. Our experiment corroborated the results, and demonstrated the efficiency of the method.

Community structure is one of the key properties of real-world complex networks. It plays a crucial role in their behaviors and topology. While an important work has been done on the issue of community detection, very little attention has been devoted to the analysis of the community structure. In this paper, we present an extensive investigation of the overlapping community network deduced from a large-scale co-authorship network. The nodes of the overlapping community network rep-resent the functional communities of the co-authorship network, and the links account for the fact that communities share some nodes in the co-authorship network. The comparative evaluation of the topological properties of these two networks shows that they share similar topological properties. These results are very interesting. Indeed, the network of communities seems to be a good representative of the original co-authorship network. With its smaller size, it may be more practical in order to realize various analyses that cannot be performed easily in large-scale real-world networks.

Introduction: Eye gaze tracking is essential in understanding non verbal communication, human-computer interaction and cognitive responses. Its applications range from healthcare to consumer behaviour analysis and gaming. The evolution of eye gaze tracking technology and its increasing adoption highlight its significance across diverse domains. Aim: To conduct a bibliometric analysis of eye gaze tracking research over the past two decades (2005-2024), exploring publication trends, collaboration patterns, key contributors and emerging research themes. Materials and Methods: The present review was a bibliometric review in which data were extracted from the Web of Science Core Collection, amounting to 9,773 peer-reviewed articles. The bibliometric analysis tools used were VOSviewer and Biblioshiny for the investigation of co-authorship, citation and co-occurrence network analysis. The critical conceptual and intellectual trends in research on eye gaze tracking were identified, focusing on publication output and global collaborations in research. Results: Findings showed that more than 62% of publications were published within the period 2018-2024. The United States of America (USA) accounted for the majority of research contributions, followed by England and Germany. Applications in healthcare, marketing and cognitive sciences were evident, with “autism” being a focus of critical importance. Conclusion: Eye gaze tracking has seen rapid growth since 2018, with an increasing focus on Artificial Intelligence (AI)- assisted applications in healthcare, assistive technology and marketing. Emerging trends such as deep learning-based eye movement prediction and gaze-driven user experiences are shaping future developments. Upcoming research is expected to integrate AI, neuroscience and human-computer interaction to advance diagnostics and gaze-based security solutions.

This study aims to explore the influence of co-author network on team knowledge creation. Integrating the two traditional perspectives of network relationship and network structure, we examine the direct and interactive effects of tie stability and structural holes on team knowledge creation. Tracking scientific articles published by 111 scholars in the research field of human resource management from the top 8 American universities, we analyze scholars’ scientific co-author networks. The result indicates that tie stability changes the teams’ information processing modes and, when graphed, results in an inverted U-shape relationship between tie stability and team knowledge creation. Moreover, structural holes in co-author network are proved to be harmful to team knowledge sharing and diffusion, thereby impeding team knowledge creation. Also, tie stability and structural hole interactively influence team knowledge creation. When the number of structural hole is low in the co-author network, the graphical representation of the relationship between tie stability and team knowledge creation tends to be a more distinct U-shape.

This paper details the development and implementation of CiteGraph, a system for constructing large-scale citation and co-authorship networks from full-text biomedical articles. CiteGraph represents articles and authors by uniquely identified nodes, and connects those nodes through citation and co-authorship relations. CiteGraph network encompasses over 1.65 million full-text articles and 6.35 million citations by 1.37 million unique authors from the Elsevier full-text articles. Our evaluation shows 98% 99% F1-score for mapping a citation to the corresponding article and identifying MEDLINE articles. We further analyzed the characteristics of CiteGraph and found that they are consistent with assumptions made using small-scale bibliometric analysis. We also developed several novel network-based methods for analyzing publication, citation and collaboration patterns. This is the first work to develop a completely automated system for the creation of a large-scale citation network in the biomedical domain, and also to introduce novel findings in researcher publication histories. CiteGraph can be a useful resource to both the biomedical community, and bibliometric research.

<p>Research collaboration is one of the strength in research management due to its advantages in quantity and quality of the research. Co-authorship network is one of the proxies to evaluate the emerging research collaborations. Co-authorship that happens for the first time among a pair of author plays an important role as the key of success for their co-authorship in the future. Therefore, the research aims to build a model predicting new co-authorship as potential co-authorship. This research used scientific articles in Indonesian biodiversity research published in Scopus during 2006-2015. New co-authorship of between 4,628 pair of authors were analyzed in terms of their similarity in co-authorship network, research interest, and community to predict whether a pair of author will have a new co-authorship in future. Random forest classifier was used to build the model after applying 10-fold cross validation in various parameter and random undersampling technique as preprocessing procedures. The result shows that the similarity in network, community network, and research interest and becomes good features to predict the potential co-authorship among a pair of author. Furthermore, paired authors that predicted to be co-authored and involving authors from Indonesian Institute of Sciences are identified as the potential patners recommended for development of research teams.</p>

Transition and transformation: A bibliometric analysis of two scientific networks researching socio-technical change