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Topic-based PageRank on author cocitation networks.

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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]
Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Topic-based PageRank on author cocitation networks.
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  Data: <searchLink fieldCode="AR" term="%22Ying+Ding%22">Ying Ding</searchLink><relatesTo>1</relatesTo><i> dingying@indiana.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Society+for+Information+Science+%26+Technology%22">Journal of the American Society for Information Science & Technology</searchLink>. Mar2011, Vol. 62 Issue 3, p449-466. 18p. 1 Diagram, 13 Charts, 3 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Information+retrieval+software%22">Information retrieval software</searchLink><br /><searchLink fieldCode="DE" term="%22Bibliographical+citations%22">Bibliographical citations</searchLink><br /><searchLink fieldCode="DE" term="%22Publications%22">Publications</searchLink><br /><searchLink fieldCode="DE" term="%22H-index+%28Citation+analysis%29%22">H-index (Citation analysis)</searchLink><br /><searchLink fieldCode="DE" term="%22Rankings+of+websites%22">Rankings of websites</searchLink><br /><searchLink fieldCode="DE" term="%22Website+authoring+programs%22">Website authoring programs</searchLink><br /><searchLink fieldCode="DE" term="%22Citation+networks%22">Citation networks</searchLink><br /><searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+resources%22">Computer network resources</searchLink>
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  Label: Abstract
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  Data: 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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  Data: <i>Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/asi.21467
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        Text: English
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      – SubjectFull: Information retrieval software
        Type: general
      – SubjectFull: Bibliographical citations
        Type: general
      – SubjectFull: Publications
        Type: general
      – SubjectFull: H-index (Citation analysis)
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      – SubjectFull: Rankings of websites
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      – SubjectFull: Website authoring programs
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      – SubjectFull: Citation networks
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      – SubjectFull: Bibliometrics
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      – SubjectFull: Algorithms
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      – SubjectFull: Computer network resources
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      – TitleFull: Topic-based PageRank on author cocitation networks.
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              Text: Mar2011
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              Y: 2011
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