Automatic network clustering via density-constrained optimization with grouping operator |
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| Affiliation: | 1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an, Shaanxi Province 710071, China;2. Information and System Laboratory, Department of Electronic Engineering, University of New Orleans, USA;1. School of Software Engineering, Chongqing University, Chongqing 400044, PR China;2. School of Computing, National University of Singapore, Singapore 117417, Singapore;3. School of Information Science and Engineering, Lanzhou University, Gansu 730000, PR China;4. Faculty of Computer and Information Science, Southwest University, Chongqing 400715, PR China;5. Faculty of Engineering, The University of Sydney, Sydney 2006, Australia;1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China;2. Stony Brook University, United States |
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| Abstract: | Automatic network clustering is an important technique for mining the meaningful communities (or clusters) of a network. Communities in a network are clusters of nodes where the intra-cluster connection density is high and the inter-cluster connection density is low. The most popular scheme of automatic network clustering aims at maximizing a criterion function known as modularity in partitioning all the nodes into clusters. But it is found that the modularity suffers from the resolution limit problem, which remains an open challenge. In this paper, the automatic network clustering is formulated as a constrained optimization problem: maximizing a criterion function with a density constraint. With this scheme, the established algorithm can be free from the resolution limit problem. Furthermore, it is found that the density constraint can improve the detection accuracy of the modularity optimization. The efficiency of the proposed scheme is verified by comparative experiments on large scale benchmark networks. |
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| Keywords: | Automatic network clustering Density-based technique Community detection Graph partitioning Constrained optimization |
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