Co-authorship networks, an important type of social net- works, 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.
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