Date: Monday, April 8, 2019

Time: 2:30 pm - 4:00 pm

Location: 3105, Lambton Tower

 

Academic networks are derived from scholarly data. They are heterogeneous in the sense that different types of nodes are involved, such as papers and authors. We study such heterogeneous networks for measuring the academic influence of authors. 

 

Academic influence has been traditionally measured by citation counts and other metrics derived from it, such as H-Index and G-Index. PageRank based algorithms have been used to give higher weight to citations from more influential papers. Paper citation network and author citation network are widely used to rank authors. A better way is to combine the two networks so that the importance of authors and papers are evaluated recursively within the heterogeneous author-paper network. This presentation will discuss the limitation of different academic networks and propose a new framework to identify influential authors. Experiments show that our approach outperforms existing methods and can identify more influential authors in Computer Science domain and Health domain. We also analyze the relations and connections between different methods.

 

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