Kernel spectral clustering for big data networks

Raghvendra Mall, Rocco Langone, Johan A.K. Suykens

Research output: Contribution to journalArticle

53 Citations (Scopus)

Abstract

This paper shows the feasibility of utilizing the Kernel Spectral Clustering (KSC) method for the purpose of community detection in big data networks. KSC employs aprimal-dual framework to construct a model. It results in a powerful property of effectively inferring the community affiliation for out-of-sample extensions. The original large kernelmatrix cannot fitinto memory. Therefore, we select a smaller subgraph that preserves the overall community structure to construct the model. It makes use of the out-of-sampleextension property for community membership of the unseen nodes. We provide anovel memory- and computationally efficient model selection procedure based on angular similarity in the eigenspace. We demonstrate the effectiveness of KSC on large scalesynthetic networks and real world networks like the YouTube network, a road network ofCalifornia and the Livejournal network. These networks contain millions of nodes and several million edges.

Original languageEnglish
Pages (from-to)1567-1586
Number of pages20
JournalEntropy
Volume15
Issue number5
DOIs
Publication statusPublished - May 2013

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Keywords

  • Angular similarity
  • Kernel spectral clustering
  • Out-of-sample extensions
  • Sampling graphs

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Mall, R., Langone, R., & Suykens, J. A. K. (2013). Kernel spectral clustering for big data networks. Entropy, 15(5), 1567-1586. https://doi.org/10.3390/e15051567