Literature search through mixed-membership community discovery

Authors:
Tina Eliassi-Rad;Keith Henderson
Affiliations:
Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA
Venue:
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Year:
2010

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Abstract

We introduce a new approach to literature search that is based on finding mixed-membership communities on an augmented co-authorship graph (ACA) with a scalable generative model. An ACA graph contains two types of edges: (1) coauthorship links and (2) links between researchers with substantial expertise overlap. Our solution eliminates the biases introduced by either looking at citations of a paper or doing a Web search. A case study on PubMed shows the benefits of our approach.