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Local expansion and optimization for higher-order graph clustering

Citation

Ma, W and Cai, L and He, T and Chen, L and Cao, Z and Li, R, Local expansion and optimization for higher-order graph clustering, IEEE Internet of Things Journal pp. 1-12. ISSN 2327-4662 (2019) [Refereed Article]


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Copyright Statement

Copyright 2019 IEEE.

DOI: doi:10.1109/JIOT.2019.2923228

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 Details

Item Type:Refereed Article
Keywords:community detection, community search, higher-order graph clustering, hypergraph clustering, motif clustering
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Pattern Recognition and Data Mining
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
UTAS Author:Cao, Z (Mr Zehong Cao)
ID Code:133217
Year Published:2019
Web of Science® Times Cited:2
Deposited By:Information and Communication Technology
Deposited On:2019-06-19
Last Modified:2019-08-30
Downloads:3 View Download Statistics

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