Discovering Communities in Linked Data by Multi-view Clustering

Abstract. We consider the problem of finding communities in large linked networks such as web structures or citation networks. We review similarity measures for linked objects and discuss the k-Means and EM algorithms, based on text similarity, bibliographic coupling, and co-citation strength. We study the utilization of the principle of multi-view learning to combine these similarity measures. We explore the clustering algorithms experimentally using web pages and the CiteSeer repository of research papers and find that multi-view clustering effectively combines link-based and intrinsic similarity.

• Type: Conference
• Year: 2005
• Where: GFKL
• Authors: Isabel Drost, Steffen Bickel, Tobias Scheffer
• Keywords: GFKL 2005, Large Linked Networks, Similarity Measures, Text Similarity