Community detection and co-author recommendation in co-author networks
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ORIGINAL ARTICLE
Community detection and co‑author recommendation in co‑author networks Tian Jin1 · Qiong Wu2 · Xuan Ou3 · Jianjun Yu3 Received: 29 March 2019 / Accepted: 28 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract With the increasing complexity of scientific research and the expanding scale of projects, scientific research cooperation is an important trend in large-scale research. The analysis of co-authorship networks is a big data problem due to the expanding scale of the literature. Without sufficient data mining, research cooperation will be limited to a similar group, namely, a “small group”, in the co-author networks. This “small group” limits the research results and openness. However, the researchers are not aware of the existence of other researchers due to insufficient big data support. Considering the importance of discovering communities and recommending potential collaborations from a large body of literature, we propose an enhanced clustering algorithm for detecting communities. It includes the selection of an initial central node and the redefinition of the distance and iteration of the central node. We also propose a method that is based on the hilltop algorithm, which is an algorithm that is used in search engines, for recommending co-authors via link analysis. The co-author candidate set is improved by screening and scoring. In screening, the expert set formation of the hilltop algorithm is added. The score is calculated from the durations and quantity of the collaborations. Via experiments, communities can be extracted, and co-authors can be recommended from the big data of the scientific research literature. Keywords Co-author network · Community detection · Modularity · Link analysis · Hilltop algorithm
1 Introduction Due to the increasing scale of the scientific literature, the analysis of it is a big data processing problem. Via graph theory in mathematics, social network analysis (SNA) and complex network methods can be used to model co-author networks. A joint network [15] G is composed of a node set N and a link set L, where N represents the collection of authors and the nodes represent the authors of the scientific papers. If two authors co-author a paper, there is a This research is supported by NSFC Grant No.61836013 and CAS 135 Informatization Project XXH13504. * Jianjun Yu [email protected] 1
School of Electronic Information and Engineering, Beihang University, Beijing, China
2
Beijing Innovation Center for Mobility Intelligence (BICMI), Beijing, China
3
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
link between the two corresponding nodes, and L represents the collection of all these links. Using this model, community detection and co-authors’ recommendation can be conducted. Considering that cooperation has become a major trend in academic research, researchers must find more partners. The co-author recommendation method will provide more suggestions from the big data of the research commu
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