Discovering value in academic social networks: A case study in ResearchGate

Abstract

The research presented in this paper is about detecting collaborative networks inside the structure of a research social network. As case study we consider ResearchGate and SEE University academic staff. First we describe the methodology used to crawl and create an academic-academic network depending from their fields of interest. We then calculate and discuss four social network analysis centrality measures (closeness, betweenness, degree, and PageRank) for entities in this network. In addition to these metrics, we have also investigated grouping of individuals, based on automatic clustering depending from their reciprocal relationships.