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Community Detection Method Based on Node Density, Degree Centrality, and K-Means Clustering in Complex Network

by Biao Cai 1,2,*, Lina Zeng 1,*, Yanpeng Wang 1, Hongjun Li 1 and Yanmei Hu 1
1
College of Information Science &Technology, Chengdu University of Technology, Chengdu 610059, China
2
Key Laboratory of Manufacturing Process Testing Technology of Ministry of Education of China, Southwest of University of Science and Technology, Mianyang 621010, China
*
Authors to whom correspondence should be addressed.
Entropy 2019, 21(12), 1145; https://doi.org/10.3390/e21121145
Received: 4 November 2019 / Revised: 21 November 2019 / Accepted: 21 November 2019 / Published: 23 November 2019
(This article belongs to the Section Complexity)
Community detection in networks plays a key role in understanding their structures, and the application of clustering algorithms in community detection tasks in complex networks has attracted intensive attention in recent years. In this paper, based on the definition of uncertainty of node community belongings, the node density is proposed first. After that, the DD (the combination of node density and node degree centrality) is proposed for initial node selection in community detection. Finally, based on the DD and k-means clustering algorithm, we proposed a community detection approach, the density-degree centrality-jaccard-k-means method (DDJKM). The DDJKM algorithm can avoid the problem of random selection of initial cluster centers in conventional k-means clustering algorithms, so that isolated nodes will not be selected as initial cluster centers. Additionally, DDJKM can reduce the iteration times in the clustering process and the over-short distances between the initial cluster centers can be avoided by calculating the node similarity. The proposed method is compared with state-of-the-art algorithms on synthetic networks and real-world networks. The experimental results show the effectiveness of the proposed method in accurately describing the community. The results also show that the DDJKM is practical a approach for the detection of communities with large network datasets.
Keywords: community detection; CB-uncertainty (Community belongings uncertainty); DD (the combination of node density and node degree centrality); k-means community detection; CB-uncertainty (Community belongings uncertainty); DD (the combination of node density and node degree centrality); k-means
MDPI and ACS Style

Cai, B.; Zeng, L.; Wang, Y.; Li, H.; Hu, Y. Community Detection Method Based on Node Density, Degree Centrality, and K-Means Clustering in Complex Network. Entropy 2019, 21, 1145.

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