Modeling Topic-Level Academic Influence in Scientific Literatures

Abstract

Scientific articles are not born equal.Some generate an entire discipline while others make relatively fewer contributions.When reviewing scientific literatures, it would be useful to identify those important articles and understand how they influence others.In this paper, we introduce J-Index, a quantitative metric modeling topic-level academic influence.J-Index is calculated based on the novelty of each article as well as its contributions to the articles where it is cited.We devise a generative model named Reference Topic Model (RefTM) which jointly utilizes the textual content and citation information in scientific literatures.We show how to learn RefTM to discover both the novelty of each paper and the strength of each citation.Experiments on a collection of more than 420,000 research papers demonstrate that RefTM outperforms the state-of-the-art approaches in terms of topic coherence as well as prediction performance, and validate J-Index’s effectiveness of capturing topic-level academic influence in scientific literatures.

Publication
Workshops at the Thirtieth AAAI Conference on Artificial Intelligence
Li Song
Li Song
Professor, IEEE Senior Member