Link prediction for interdisciplinary collaboration via co-authorship network

We analyse the Publication and Research (PURE) data set of University of Bristol collected between $2008$ and $2013$. Using the existing co-authorship network and academic information thereof, we propose a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in a university-wide collaboration network

Prediction of scientific collaborations through multiplex interaction networks

Link prediction algorithms can help to understand the structure and dynamics of scientific collaborations and the evolution of Science. However, available algorithms based on similarity between nodes of collaboration networks are bounded by the limited amount of links present in these networks. In this work, we reduce the latter intrinsic limitation by generalizing the Adamic-Adar method to multiplex networks composed by an arbitrary number of layers, that encode diverse forms of scientific interactions. We show that the new metric outperforms other single-layered, similarity-based scores and that scientific credit, represented by citations, and common interests, measured by the usage of common keywords, can be predictive of new collaborations. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and provides a new algorithm for link prediction in multiplex networks that can be applied to a plethora of systems

Prediction of new scientific collaborations through multiplex networks

The establishment of new collaborations among scientists fertilizes the scientific environment, fostering novel discoveries. Understanding the dynamics driving the development of scientific collaborations is thus crucial to characterize the structure and evolution of science. In this work, we leverage the information included in publication records and reconstruct a categorical multiplex networks to improve the prediction of new scientific collaborations. Specifically, we merge different bibliographic sources to quantify the prediction potential of scientific credit, represented by citations, and common interests, measured by the usage of common keywords. We compare several link prediction algorithms based on different dyadic and triadic interactions among scientists, including a recently proposed metric that fully exploits the multiplex representation of scientific networks. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and validates a new algorithm that can be readily applied to link prediction in systems represented as multiplex networks. © 2021, The Author(s)

A link prediction-based method for identifying potential cooperation partners: A case study on four journals of informetrics

© 2018 Portland International Conference on Management of Engineering and Technology, Inc. (PICMET). Global academic exchange and cooperation have become an increasing trend in both academia and industry, but how to quickly and effectively identify potential partners is becoming an urgent problem. This paper proposes a link prediction-based model to help researchers identify partners from a large collection of academic articles in a given technological area. We initially construct a co-authorship network, and take a series of indices based on network and similarity of researchers into consideration. A fitting model of link prediction is then established, in which logistic regression analysis is involved. An empirical study on four journals of informetrics is conducted to demonstrate the reliability of the proposed method

Predicting the dynamics of scientific activities: A diffusion-based network analytic methodology

Copyright © 2018 by Association for Information Science and Technology With the rapid explosion of information and the dramatic development of bibliometric techniques in the past decades, it becomes a challenge to comprehensively, extensively, and efficiently understand science maps. Aim-ing to explore in-depth insights from science maps and predict the dynamics of scientific activities, this paper, based on the co-occurrence statistics of terms derived from scientific documents, proposes a diffusion-based network analytic methodology to conduct the prediction study from two aspects: the research interest of scien-tific researchers and the evolutionary directions of scientific topics. A case study on academic articles down-loaded from three leading journals in the field of bibliometrics demonstrates the feasibility of the methodology. The future directions of bibliometrics are identified, such as the application of information technologies to tradi-tional bibliometric data, the interactions between bibliometrics and science, technology, and innovation policy issues, and individual-level bibliometrics. The results also provide recommendations as potential research inter-ests for a set of experts. The proposed method could be a toolkit to conduct forecasting studies for a given technological area or a given discipline, and a recommender system to assist academic researchers in identify-ing potential research interests and extended areas

Network Analysis of Scientific Collaboration and Co-authorship of the Trifecta of Malaria, Tuberculosis and Hiv/aids in Benin.

Despite the international mobilization and increase in research funding, Malaria, Tuberculosis and HIV/AIDS are three infectious diseases that have claimed more lives in sub Saharan Africa than any other place in the World. Consortia, research network and research centers both in Africa and around the world team up in a multidisciplinary and transdisciplinary approach to boost efforts to curb these diseases. Despite the progress in research, very little is known about the dynamics of research collaboration in the fight of these Infectious Diseases in Africa resulting in a lack of information on the relationship between African research collaborators. This dissertation addresses the problem by documenting, describing and analyzing the scientific collaboration and co-authorship network of Malaria, Tuberculosis and HIV/AIDS in the Republic of Benin. We collected published scientific records from the Web Of Science over the last 20 years (From January 1996 to December 2016). We parsed the records and constructed the coauthorship networks for each disease. Authors in the networks were represented by vertices and an edge was created between any two authors whenever they coauthor a document together. We conducted a descriptive social network analysis of the networks, then used mathematical models to characterize them. We further modeled the complexity of the structure of each network, the interactions between researchers, and built predictive models for the establishment of future collaboration ties. Furthermore, we implemented the models in a shiny-based application for co-authorship network visualization and scientific collaboration link prediction tool which we named AuthorVis. Our findings suggest that each one of the collaborative research networks of Malaria, HIV/AIDS and TB has a complex structure and the mechanism underlying their formation is not random. All collaboration networks proved vulnerable to structural weaknesses. In the Malaria coauthorship network, we found an overwhelming dominance of regional and international contributors who tend to collaborate among themselves. We also observed a tendency of transnational collaboration to occur via long tenure authors. We also find that TB research in Benin is a low research productivity area. We modeled the structure of each network with an overall performance accuracy of 79.9%, 89.9%, and 93.7% for respectively the malaria, HIV/AIDS, and TB coauthorship network. Our research is relevant for the funding agencies operating and the national control programs of those three diseases in Benin (the National Malaria Control Program, the National AIDS Control Program and the National Tuberculosis Control Program)

Large-scale Multi-layer Academic Networks Derived from Statistical Publications

The utilization of multi-layer network structures now enables the explanation of complex systems in nature from multiple perspectives. Multi-layer academic networks capture diverse relationships among academic entities, facilitating the study of academic development and the prediction of future directions. However, there are currently few academic network datasets that simultaneously consider multi-layer academic networks; often, they only include a single layer. In this study, we provide a large-scale multi-layer academic network dataset, namely, LMANStat, which includes collaboration, co-institution, citation, co-citation, journal citation, author citation, author-paper and keyword co-occurrence networks. Furthermore, each layer of the multi-layer academic network is dynamic. Additionally, we expand the attributes of nodes, such as authors' research interests, productivity, region and institution. Supported by this dataset, it is possible to study the development and evolution of statistical disciplines from multiple perspectives. This dataset also provides fertile ground for studying complex systems with multi-layer structures