Understanding Importance of Collaborations in Co-authorship Networks: A Supportiveness Analysis Approach

Co-authorship networks, an important type of social networks, have been studied extensively from various angles such as degree distribution analysis, social community extraction and social entity ranking. Most of the previous studies consider the co-authorship relation between two authors as a collaboration. In this paper, we introduce a novel and interesting “supportiveness” measure on co-authorship relation. The fact that two authors co-author one paper can be regarded as one author supports the other’s scientific work. We propose several supportiveness measures, and exploit a supportiveness-based author ranking scheme. Several efficient algorithms are developed to compute the top-n most supportive authors. Moreover, we extend the supportiveness analysis to community extraction, and develop feasible solutions to identify the most supportive groups of authors. The empirical study conducted on a large real data set indicates that the supportiveness measures are interesting and meaningful, and our methods are effective and efficient in practice.