Topic-based PageRank on author cocitation networks

Authors:
Ying Ding
Affiliations:
School of Library and Information Science, Indiana University, 1320 E. 10th St., Bloomington, IN 47405
Venue:
Journal of the American Society for Information Science and Technology
Year:
2011

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Abstract

Ranking authors is vital for identifying a researcher's impact and standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The author-conference-topic (ACT) model was used to extract topic distribution of individual authors. Two ways for combining the ACT model with the PageRank algorithm are proposed: simple combination (I_PR) or using a topic distribution as a weighted vector for PageRank (PR_t). Information retrieval was chosen as the test field and representative authors for different topics at different time phases were identified. Principal component analysis (PCA) was applied to analyze the ranking difference between I_PR and PR_t. © 2011 Wiley Periodicals, Inc.