Coauthorship Network Mining for Scholar Communication and Collaboration Path Recommendation

With the increasing penetration of interdisciplinary subjects, it is more difficult for researchers to complete a paper individually, showing that the division of labor can improve the level and efficiency of scientific research. Thus, collaboration among multiple scholars has become a trend in academic research. However, because the numbers of scholars and papers are increasing and cooperation between scholars has become more frequent in recent years, it is an increasingly challenging task to discover useful knowledge resources for researchers. Against the background of big data, how to help scholars quickly find interested target collaborators, encourage them to participate more actively in academic communication, and create high-quality achievements in scientific research has become a significant problem. Considering this challenge, this article proposes a framework of coauthorship strength, author contribution, and search (CCS，taking the first letter of the keyword), which is based on the coauthorship feature of Google Academics. In CSS, we combined the search algorithm to select the optimal connection path to help scholars find interested target scholars efficiently and to better solve practical application problems. Finally, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results of our approach show better search outcomes compared to other competitive approaches.

Stars and Brokers: Knowledge Spillovers Among Medical Scientists

This paper estimates the heterogeneity in peer effects among research scientists in terms of network position. I propose a new measure, brokerage degree, that determines the extent to which a scientist depends on a coauthor to provide him unique access to other scientists further away. I apply this measure to the coauthorship network of medical scientists. I show that network position is crucial for productivity by facilitating access to nonredundant knowledge. Identification results from variation in brokerage degree among coauthors linked to a star scientist who dies. A one standard deviation increase in the brokerage degree of a deceased star is associated with a 10% decrease in annual publications of his coauthor. By applying brokerage degree to topics, I provide evidence that access to knowledge flows embodied in scientists further away can account for a large proportion of the identified heterogeneity effect. This paper was accepted by Toby Stuart, entrepreneurship and innovation.

Building visualization skills through investigating the Journal of the Medical Library Association coauthorship network from 2006–2017

Objective: The primary objective of this study was to explore different dimensions of Journal of the Medical Library Association (JMLA) authorship from 2006–2017. Dimensions that were evaluated using coauthorship networks and affiliation data included collaboration, geographical reach, and relationship between Medical Library Association (MLA) member and nonmember authors. A secondary objective was to analyze the practice and practical application of data science skills.Methods: A team of librarians who attended the 2017 Data Science and Visualization Institute used JMLA bibliographic metadata extracted from Scopus, together with select MLA membership data from 2006–2017. Data cleaning, anonymization, analysis, and visualization were done collaboratively by the team members to meet their learning objectives and to produce insights about the nature of collaborative authorship at JMLA.Results: Sixty-nine percent of the 1,351 JMLA authors from 2006–2017 were not MLA members. MLA members were more productive and collaborative, and tended to author articles together. The majority of the authoring institutions in JMLA are based in the United States. Global reach outside of the United States and Canada shows higher authorship in English-speaking countries (e.g., Australia, United Kingdom), as well as in Western Europe and Japan.Conclusions: MLA support of JMLA may benefit a wider network of health information specialists and medical professionals than is reflected in MLA membership. Conducting coauthorship network analyses can create opportunities for health sciences librarians to practice applying emerging data science and data visualization skills.

Paper Bastions: Architecting Academic Citadels from 1991 to 2009

How were paper bastions added to the walls of academic citadels? By mapping the evolution of the coauthorship network in 180 management journals from 1991 to 2009, we identify an elite league of business schools that retained dominance despite the research community’s significant growth. The elite universities maintain their prominence through a loop of reinforcement involving the peer review process and third-party ranking bodies, though the perceived quality of the papers published was declining as measured by the percentage of overall citations. Leading U.S. universities dominate top journal publications, while new local poles of management research among European and Asian universiteis emerged.

Coauthorship in Regional Science

The year 2015 marked the fiftieth anniversary of West Virginia University’s (WVU) Regional Research Institute (RRI), which has played an important role in many scientific collaboration networks. Through social network analysis (SNA) focusing on the RRI research community since its inception in 1965, this article illustrates the role that organizations and the networks they promote can play in scientific problem domains, promoting scholarly collaborations and coauthorship in the field of regional science. We analyzed an evolving WVU RRI coauthorship network that has grown and gained in complexity over time in terms of (1) global metrics, (2) components and cluster analysis, (3) centrality, and (4) PageRank and AuthorRank. The results of these analyses depict a well-developed and influential scientific collaboration structure within both WVU and the regional science research community.

Comparação de Técnicas de Predição de Links em Sub-redes de Coautoria Formada por Currículos da Plataforma Lattes

The study of Lattes platform allows addressing and analyzing Brazil researchers network which could be useful for defining politics to improve science, technology, and innovation. This work evaluated Lattes Platform coauthorship network. This network evolves over time, which means that new coauthorships will arise in future. Therefore, using link prediction methods in this network would help to identify growing knowledge areas in Brazil. The used technics were Spectral Evolution, wich is new in this context, Common Neighbors, Adamic-Adar and Jaccard. The main goal was to evaluate the link prediction accuracy with different methods at the coauthorship network of Lattes Platform. The Spectral Evolution was worse than the others. Adamic-Adar method presented the best result - 817 times better than the random link prediction.

Coauthorship network-based literature recommendation with topic model

Purpose The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of articles. Previous works on recommending articles to satisfy users’ short-term interests have utilized article content, usage logs, and more recently, coauthorship networks. The usefulness of coauthorship has been demonstrated by some research works, which, however, tend to adopt a simple coauthorship network that records only the strength of coauthorships. The purpose of this paper is to enhance the effectiveness of coauthorship-based recommendation by incorporating scholars’ collaboration topics into the coauthorship network. Design/methodology/approach The authors propose a latent Dirichlet allocation (LDA)-coauthorship-network-based method that integrates topic information into the links of the coauthorship networks using LDA, and a task-focused technique is developed for recommending literature articles. Findings The experimental results using information systems journal articles show that the proposed method is more effective than the previous coauthorship network-based method over all scenarios examined. The authors further develop a hybrid method that combines the results of content-based and LDA-coauthorship-network-based recommendations. The resulting hybrid method achieves greater or comparable recommendation effectiveness under all scenarios when compared to the content-based method. Originality/value This paper makes two contributions. The authors first show that topic model is indeed useful and can be incorporated into the construction of coaurthoship-network to improve literature recommendation. The authors subsequently demonstrate that coauthorship-network-based and content-based recommendations are complementary in their hit article rank distributions, and then devise a hybrid recommendation method to further improve the effectiveness of literature recommendation.