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11

GFKL
2005
Springer

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Discovering Communities in Linked Data by Multi-view Clustering

13 years 10 months ago

Discovering Communities in Linked Data by Multi-view Clustering

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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.

Isabel Drost, Steffen Bickel, Tobias Scheffer

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GFKL 2005 | Large Linked Networks | Similarity Measures | Text Similarity |

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Added	27 Jun 2010
Updated	27 Jun 2010
Type	Conference
Year	2005
Where	GFKL
Authors	Isabel Drost, Steffen Bickel, Tobias Scheffer

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