2006 ClustScientificLiteratureUsingSparseCitationGraphAnalysis

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Subject Headings: CiteSeerX, Linked Data, Bibliometrics.


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It is well known that connectivity analysis of linked documents provides significant information about the structure of the document space for unsupervised learning tasks. However, the ability to identify distinct clusters of documents based on link graph analysis is proportional to the density of the graph and depends on the availability of the linking and/or linked documents in the collection. In this paper, we present an information theoretic approach towards measuring the significance of individual words based on the underlying link structure of the document collection. This enables us to generate a non-uniform weight distribution of the feature space which is used to augment the original corpus-based document similarities. The experimental results on the collection of scientific literature show that our method achieves better separation of distinct groups of documents, yielding improved clustering solutions.


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2006 ClustScientificLiteratureUsingSparseCitationGraphAnalysisLevent Bolelli
Seyda Ertekin
C. Lee Giles
Clustering Scientific Literature Using Sparse Citation Graph AnalysisProceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databaseshttp://web.mit.edu/seyda/www/Papers/PKDD06 SparseCitationGraph.pdf10.1007/11871637_82006