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2017 IEEE International Conference on Data Mining (ICDM) (2017)
New Orleans, Louisiana, USA
Nov. 18, 2017 to Nov. 21, 2017
ISSN: 2374-8486
ISBN: 978-1-5386-3835-4
pp: 41-50
Detection of interesting (e.g., coherent or anomalous) clusters has been studied extensively on plain or univariate networks, with various applications. Recently, algorithms have been extended to networks with multiple attributes for each node in the real-world. In a multi-attributed network, often, a cluster of nodes is only interesting for a subset (subspace) of attributes, andthis type of clusters is called subspace clusters. However, in the current literature, few methods are capable of detecting subspace clusters, which involves concurrent feature selection and network cluster detection. These relevant methods are mostly heuristic-driven and customized for specific application scenarios. In this work, we present a generic and theoretical framework for detection of interesting subspace clusters in large multi-attributed networks. Specifically, we propose a subspace graph-structured matching pursuit algorithm, namely, SG-Pursuit, to address a broad class of such problems for different scorefunctions (e.g., coherence or anomalous functions) and topology constraints (e.g., connected subgraphs and dense subgraphs). We prove that our algorithm 1) runs in nearly-linear time on the network size and the total number of attributes and 2) enjoys rigorous guarantees (geometrical convergence rate and tight error bound) analogous to those of the state-of-the-art algorithms for sparse feature selection problems and subgraph detection problems. As a case study, we specialize SG-Pursuit to optimizea number of well-known score functions for two typical tasks, including detection of coherent dense and anomalous connected subspace clusters in real-world networks. Empirical evidence demonstrates that our proposed generic algorithm SG-Pursuit is superior over state-of-the-art methods that are designed specifically for these two tasks.
computational complexity, data mining, feature selection, graph theory, iterative methods, pattern clustering, social networking (online)

F. Chen, B. Zhou, A. Alim and L. Zhao, "A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks," 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, Louisiana, USA, 2018, pp. 41-50.
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