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Topic-based heterogeneous rank

delete2015-05-09
delete44
PRE
AI
T
Tehmina Amjad *
Ying Ding 封面图
Ying Ding (Ying Ding)
A
Ali Daud
徐
徐健 (Jian Xu)
V
Vincent Malic
DOI:10.1007/s11192-015-1601-ydelete
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摘要

摘要

En 中文
Topic-based ranking of authors, papers and journals can serve as a vital tool for identifying authorities of a given topic within a particular domain. Existing methods that measure topic-based scholarly output are limited to homogeneous networks. This study proposes a new informative metric called Topic-based Heterogeneous Rank (TH Rank) which measures the impact of a scholarly entity with respect to a given topic in a heterogeneous scholarly network containing authors, papers and journals. TH Rank calculates topic-dependent ranks for authors by considering the combined impact of the multiple factors which contribute to an author's level of prestige. Information retrieval serves as the test field and articles about information retrieval published between 1956 and 2014 were extracted from web of science. Initial results show that TH Rank can effectively identify the most prestigious authors, papers and journals related to a specific topic.
Keyword:
Topic-based rank
Topic sensitive ranking
Heterogeneous networks
Topic modeling
AI总结

AI总结

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期刊

Scientometrics 封面图
Scientometrics
IF:
3.5
论文数:
8.1K
被引数:
2.2W

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indiana university system
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international islamic university, pakistan
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Indiana University Bloomington
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被引数: 2.8W
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