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Kernel Spectral Clustering for Big Data Networks

Department of Electrical Engineering ESAT/SCD (SISTA), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium
Author to whom correspondence should be addressed.
Entropy 2013, 15(5), 1567-1586;
Received: 1 March 2013 / Revised: 25 April 2013 / Accepted: 29 April 2013 / Published: 3 May 2013
(This article belongs to the Special Issue Big Data)
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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 a primal-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 kernel matrix 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-sample extension property for community membership of the unseen nodes. We provide a novel memory- and computationally efficient model selection procedure based on angular similarity in the eigenspace. We demonstrate the effectiveness of KSC on large scale synthetic networks and real world networks like the YouTube network, a road network of California and the Livejournal network. These networks contain millions of nodes and several million edges. View Full-Text
Keywords: kernel spectral clustering; out-of-sample extensions; sampling graphs; angular similarity kernel spectral clustering; out-of-sample extensions; sampling graphs; angular similarity

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Mall, R.; Langone, R.; Suykens, J.A. Kernel Spectral Clustering for Big Data Networks. Entropy 2013, 15, 1567-1586.

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