Abstract
We introduce a new approach to literature search that is based on finding mixed-membership communities on an augmented coauthorship 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. © Springer-Verlag Berlin Heidelberg 2010.
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CITATION STYLE
Eliassi-Rad, T., & Henderson, K. (2010). Literature search through mixed-membership community discovery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6007 LNCS, pp. 70–78). https://doi.org/10.1007/978-3-642-12079-4_11
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