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Abstract

Graph clustering aims to identify clusters that feature tighter connections between internal nodes than external nodes. We noted that conventional clustering approaches based on a single vertex or edge cannot meet the requirements of clustering in a higher-order mixed structure formed by multiple nodes in a complex network. Considering the above limitation, we are aware of the fact that a clustering coefficient can measure the degree to which nodes in a graph tend to cluster, even if only a small area of the graph is given. In this study, we introduce a new cluster quality score, i.e., the local motif rate, which can effectively respond to the density of clusters in a higher-order graph. We also propose a motif-based local expansion and optimization algorithm (MLEO) to improve local higher-order graph clustering. This algorithm is a purely local algorithm and can be applied directly to higher-order graphs without conversion to a weighted graph, thus avoiding distortion of the transform. In addition, we propose a new seed-processing strategy in a higher-order graph. The experimental results show that our proposed strategy can achieve better performance than the existing approaches when using a quadrangle as the motif in the LFR network and the value of the mixing parameter μ exceeds 0.6.

Item Type:	Article
Authors/Creators:	Ma, W and Cai, L and He, T and Chen, L and Cao, Z and Li, R
Keywords:	community detection, community search, higher-order graph clustering, hypergraph clustering, motif clustering
Journal or Publication Title:	IEEE Internet of Things Journal
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2327-4662
DOI / ID Number:	https://doi.org/10.1109/JIOT.2019.2923228
Copyright Information:	Copyright 2019 IEEE.
Related URLs:	Google Scholar: Cao, Z
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