Pagerank based clustering of hypertext document collections

Konstantin Avrachenkov (1) , Vladimir Dobrynin (2) , Danil Nemirovsky (1) , Son Kim Pham (3) , Elena Smirnova (2)

1 MAESTRO - Models for the performance analysis and the control of networks
2 SPbU - St Petersburg State University
3 UC San Diego - University of California [San Diego]

Clustering hypertext document collection is an important task in Information Retrieval. Most clustering methods are based on document content and do not take into account the hyper-text links. Here we propose a novel PageRank based clustering (PRC) algorithm which uses the hypertext structure. The PRC algorithm produces graph partitioning with high modularity and coverage. The comparison of the PRC algorithm with two content based clustering algorithms shows that there is a good match between PRC clustering and content based clustering.

Domaines: Réseaux et télécommunications [cs.NI]

Konstantin Avrachenkov, Vladimir Dobrynin, Danil Nemirovsky, Son Kim Pham, Elena Smirnova. Pagerank based clustering of hypertext document collections. International ACM SIGIR Conference on Research & Development in Information Retrieval, Jul 2008, Singapore, Singapore. pp.873--874, ⟨10.1145/1390334.1390549⟩. ⟨inria-00565355⟩