Overlap Community Detection Based on Node Convergence Degree

Weimin Li; Huai Kou Miao; Qun Jin; Shu Jiang; Qun Jin

doi:10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46

Overlap Community Detection Based on Node Convergence Degree

Weimin Li, Huai Kou Miao, Qun Jin, Shu Jiang, Qun Jin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology with the node attributes. In our method, PageRank algorithm is used to get the importance of each node in the global network and utilize the local network (local neighbors) to measure the structure convergence degree. Then, node convergence degree combining node attributes and structure convergence degree is designed. Finally, the overlap communities can be identified by the Spectral Cluster based on node convergence degree. Experiments results demonstrate effectiveness and better performance of our method.

Original language	English
Title of host publication	Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	163-167
Number of pages	5
ISBN (Electronic)	9781509040650
DOIs	https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46
Publication status	Published - 2016 Oct 11
Event	14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016 - Auckland, New Zealand Duration: 2016 Aug 8 → 2016 Aug 10

Other

Other	14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
Country/Territory	New Zealand
City	Auckland
Period	16/8/8 → 16/8/10

Keywords

community structure
Node convergence degree
overlap
PageRank

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Information Systems
Computer Science (miscellaneous)
Artificial Intelligence
Computer Networks and Communications

Access to Document

10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46

Cite this

Li, W., Miao, H. K., Jin, Q., Jiang, S., & Jin, Q. (2016). Overlap Community Detection Based on Node Convergence Degree. In Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016 (pp. 163-167). [7588840] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46

Overlap Community Detection Based on Node Convergence Degree. / Li, Weimin; Miao, Huai Kou; Jin, Qun et al.

Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 163-167 7588840.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, W, Miao, HK, Jin, Q, Jiang, S & Jin, Q 2016, Overlap Community Detection Based on Node Convergence Degree. in Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016., 7588840, Institute of Electrical and Electronics Engineers Inc., pp. 163-167, 14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016, Auckland, New Zealand, 16/8/8. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46

Li W, Miao HK, Jin Q, Jiang S, Jin Q. Overlap Community Detection Based on Node Convergence Degree. In Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 163-167. 7588840 doi: 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.46

Li, Weimin ; Miao, Huai Kou ; Jin, Qun et al. / Overlap Community Detection Based on Node Convergence Degree. Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 163-167

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abstract = "Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology with the node attributes. In our method, PageRank algorithm is used to get the importance of each node in the global network and utilize the local network (local neighbors) to measure the structure convergence degree. Then, node convergence degree combining node attributes and structure convergence degree is designed. Finally, the overlap communities can be identified by the Spectral Cluster based on node convergence degree. Experiments results demonstrate effectiveness and better performance of our method.",

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N2 - Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology with the node attributes. In our method, PageRank algorithm is used to get the importance of each node in the global network and utilize the local network (local neighbors) to measure the structure convergence degree. Then, node convergence degree combining node attributes and structure convergence degree is designed. Finally, the overlap communities can be identified by the Spectral Cluster based on node convergence degree. Experiments results demonstrate effectiveness and better performance of our method.

AB - Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology with the node attributes. In our method, PageRank algorithm is used to get the importance of each node in the global network and utilize the local network (local neighbors) to measure the structure convergence degree. Then, node convergence degree combining node attributes and structure convergence degree is designed. Finally, the overlap communities can be identified by the Spectral Cluster based on node convergence degree. Experiments results demonstrate effectiveness and better performance of our method.

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Y2 - 8 August 2016 through 10 August 2016

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Overlap Community Detection Based on Node Convergence Degree

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