Analyzing the Impact of Social Collaborations on Influence Identification in Scientific Literature Analytic: An Analysis on ResearchGate and Academia

Influence identification, one of the compelling applications of Social Network Analysis (SNA) is gaining immense attention in scientific literature analytics. Existing influence identification techniques in the scientific domain majorly explore scientific collaborations (co-author and co-citation networks) of researchers. Standard centrality algorithms are widely applied for this purpose. The emergence of digital scholarly platforms allows researchers to build their social community in the scientific environment. Few scholarly platforms maintain social media like follower and following relations apart from co-authorships and co-citations of researchers. This research examines the impact of followers and followings on influence identification in the scientific domain. The real scientific information from widely utilized digital scholarly platforms: ResearchGate (RG) and Academia is extracted. From the collected information, scholarly networks are constructed based on follower-following relations. Standard centrality algorithms are implemented to identify the influence of these networks. The results are compared with i) the researcher’s influence scores provided by RG and Academia ii) three legitimate global ranking lists of researchers. The outcome suggested that, like SNA, social collaborations among researchers in terms of followers and followings significantly impact influence identification in the scientific domain.