Efficient eigen-updating for spectral graph clustering

Charanpal Dhanjal

doi:10.1016/j.neucom.2013.11.015

2014-05-01

Charanpal Dhanjal *

Romaric Gaudel

Stéphan Clémençon

DOI：10.1016/j.neucom.2013.11.015

原文链接

查看原文

摘要

En 中文

Partitioning a graph into groups of vertices such that those within each group are more densely connected than vertices assigned to different groups, known as graph clustering, is often used to gain insight into the organisation of large scale networks and for visualisation purposes. Whereas a large number of dedicated techniques have been recently proposed for static graphs, the design of on-line graph clustering methods tailored for evolving networks is a challenging problem, and much less documented in the literature. Motivated by the broad variety of applications concerned, ranging from the study of biological networks to the analysis of networks of scientific references through the exploration of communications networks such as the World Wide Web, it is the main purpose of this paper to introduce a novel, computationally efficient, approach to graph clustering in the evolutionary context. Namely, the method promoted in this article can be viewed as an incremental eigenvalue solution for the spectral clustering method described by Ng et al. (2001) [25]. The incremental eigenvalue solution is a general technique for finding the approximate eigenvectors of a symmetric matrix given a change. As well as outlining the approach in detail, we present a theoretical bound on the quality of the approximate eigenvectors using perturbation theory. We then derive a novel spectral clustering algorithm called Incremental Approximate Spectral Clustering (IASC). The IASC algorithm is simple to implement and its efficacy is demonstrated on both synthetic and real datasets modelling the evolution of a HIV epidemic, a citation network and the purchase history graph of an e-commerce website. (C) 2014 Elsevier B.V. All rights reserved.

Keyword:

Spectral graph clustering

Eigen-decomposition

Unsupervised learning

Normalised Laplacian