Topic-based PageRank on author cocitation networks.

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Bibliographic Details
Title:	Topic-based PageRank on author cocitation networks.
Authors:	Ying Ding¹ dingying@indiana.edu
Source:	Journal of the American Society for Information Science & Technology. Mar2011, Vol. 62 Issue 3, p449-466. 18p. 1 Diagram, 13 Charts, 3 Graphs.
Subjects:	Information retrieval software, Bibliographical citations, Publications, H-index (Citation analysis), Rankings of websites, Website authoring programs, Citation networks, Bibliometrics, Algorithms, Computer network resources
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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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. [ABSTRACT FROM AUTHOR]
ISSN:	15322882
DOI:	10.1002/asi.21467