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Measuring relatedness between communities in a citation network.
- Source :
-
Journal of the American Society for Information Science & Technology . Jul2011, Vol. 62 Issue 7, p1360-1369. 10p. - Publication Year :
- 2011
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15322882
- Volume :
- 62
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Journal of the American Society for Information Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 61352395
- Full Text :
- https://doi.org/10.1002/asi.21477