Measuring relatedness between communities in a citation network

Naoki Shibata, Yuya Kajikawa and Ichiro Sakata

Journal of the American Society for Information Science and Technology, 2011, vol. 62, issue 7, 1360-1369

Abstract: As academic disciplines are segmented and specialized, it becomes more difficult to capture relevant research areas precisely by common retrieval strategies using either keywords or journal categories. This paper proposes a method of measuring the relatedness among sets of academic papers in order to detect unrelated communities which are not related to target topic. A citation network, extracted by given keywords, is divided into communities based on the density of links. We measured and compared four measures of relatedness between two communities in a citation network for three large‐scale citation datasets. We used both link and semantic similarities. The topological distance from the center in a citation network is a more efficient measure for removing the unrelated communities than the other three measures: the ratio of the number of intercluster links over the all links, the ratio of the number of common terms over all terms, cosine similarity of tf‐idf vectors.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:62:y:2011:i:7:p:1360-1369

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