Ranking Scientific Articles by Exploiting Citations, Authors, Journals, and Time Information

Ranking scientific articles is an important but challenging task, partly due to the dynamic nature of the evolving publication network. In this paper, we mainly focus on two problems: (1) how to rank articles in the heterogeneous network; and (2) how to use time information in the dynamic network in order to obtain a better ranking result. To tackle the problems, we propose a graphbased ranking method, which utilizes citations, authors, journals/conferences and the publication time information collaboratively. The experiments were carried out on two public datasets. The result shows that our approach is practical and ranks scientific articles more accurately than the state-of-art methods.

Authors

• Yujing Wang Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Artificial Intelligence, Beihang University, China
• Yunhai Tong Peking University
• Ming Zeng Xiamen University

Keywords

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