Influential Analysis in Micro Scholar Social Networks

Scholar citation is a basic activity in scientific community. Some academic search engines have been developed in Web such as Google Scholar and Microsoft Academic Search. Efficient flexible querying method is essential for researchers to effectively follow trends within related topics of their research field. In this paper, we propose a procedure to construct Micro Scholar Social Networks (MSSN) from Google Scholar and then develop a querying and ranking method to find the influential researchers or articles in MSSN. An extension to the Follow Model (Extended Follow Model) is proposed in this paper and applied to describe the paper citation and author-follow relationships. It is also coupled with different ranking algorithms, namely, PageRank, AuthorRank and InventorRank to study a MSSN in Air Traffic Management. The case study shows that Extended Follow Model is robust and efficient for ranking and mining a heterogeneous academic network. In spite the fact that study was done on Google Scholar, but the proposed data mining method is applicable for other academic search engines.