Ranking academic influence with integration of weighted PageRank and HITS for paper citation network
by Kun Ma; Qiang Wei; Weijuan Zhang; Yue Lu; Ajith Abraham
International Journal of Grid and Utility Computing (IJGUC), Vol. 15, No. 6, 2024

Abstract: Paper influence analysis of a Paper Citation Network (PCN) is an indispensable technique to support fancy literature management system. Currently, it is challenging to find representative papers in an ocean of papers. This paper proposes a ranking-based influence analysis with integration of weighted PageRank and Hyperlink-Induced Topic Search (HITS) for a paper citation network. Contributions of our paper are weighted paper citation network with degree and tie strength, further feature extraction of influence of paper and author, integration of weighted PageRank and HITS, and ranking-based influence analysis. The experiments show the effectiveness of the influence analysis of the paper propagation. Our ranking-based influence analysis achieves the accuracy of 79.19% and 76.31% for Computer Science and Materials Science respectively.

Online publication date: Wed, 20-Nov-2024

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