Topic-Sensitive Influential Paper Discovery in Citation Network

TLDR: This work creatively combine both topics of text and the influence of topics over citation networks to discover influential articles, and proposes MTID, a scalable generative model, which generates the network with these two parameters.

Abstract: Discovering important papers in different academic topics is known as topic-sensitive influential paper discovery. Previous works mainly find the influential papers based on the structure of citation networks but neglect the text information, while the text of documents gives a more precise description of topics. In our paper, we creatively combine both topics of text and the influence of topics over citation networks to discover influential articles. The observation on three standard citation networks shows that the existence of citations between papers is related to the topic of citing papers and the importance of cited papers. Based on this finding, we introduce two parameters to describe the topic distribution and the importance of a document. We then propose MTID, a scalable generative model, which generates the network with these two parameters. The experiment confirms superiority of MTID over other topic-based methods, in terms of at least 50% better citation prediction in recall, precision and mean reciprocal rank. In discovering influential articles in different topics, MTID not only identifies papers with high citations, but also succeeds in discovering other important papers, including papers about standard datasets and the rising stars.