Ranking users, papers and authors in online scientific communities

摘要

The ever-increasing quantity and complexity of scientific production havemade it difficult for researchers to keep track of advances in their ownfields. This, together with growing popularity of online scientificcommunities, calls for the development of effective information filteringtools. We propose here a method to simultaneously compute reputation of usersand quality of scientific artifacts in an online scientific community.Evaluation on artificially-generated data and real data from the EconophysicsForum is used to determine the method's best-performing variants. We show thatwhen the method is extended by considering author credit, its performanceimproves on multiple levels. In particular, top papers have higher citationcount and top authors have higher $h$-index than top papers and top authorschosen by other algorithms.