Extending CDFR for overlapping community detection

Lu, N; Luo, W; Ni, L; Jiang, H; Ding, W

Extending CDFR for overlapping community detection

Lu, N Luo, W Ni, L Jiang, H Ding, W

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018, 2018, pp. 200 - 206
Issue Date:: 2018-05-25

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Field	Value	Language
dc.contributor.author	Lu, N	en_US
dc.contributor.author	Luo, W	en_US
dc.contributor.author	Ni, L	en_US
dc.contributor.author	Jiang, H	en_US
dc.contributor.author	Ding, W	en_US
dc.date.issued	2018-05-25	en_US
dc.identifier.citation	Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018, 2018, pp. 200 - 206	en_US
dc.identifier.isbn	9781538657621	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133854
dc.description.abstract	© 2018 IEEE. In many real-world networks, a node often belongs to multiple communities. Therefore, overlapping community detection is an important task in social network analysis. CDFR is an efficient algorithm recently proposed for non-overlapping community detection. In this paper, CDFR is extended for overlapping community detection, and the extended algorithm is called as OCDFR. In OCDFR, CDFR is first called to obtain a non-overlapping community partition. Then, find the kth NGC (k=1, 2, 3,..) node for each node, and record the fuzzy relations between them. Based on the fuzzy relation between a node and its kth NGC, five decision methods are proposed to determine whether it can join the communities to which its kth NGC belongs. Experimental results on real-world networks and synthetic networks demonstrate that OCDFR is effective and highly competitive.	en_US
dc.relation.ispartof	Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018	en_US
dc.relation.isbasedon	10.1109/ICDIS.2018.00040	en_US
dc.title	Extending CDFR for overlapping community detection	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. In many real-world networks, a node often belongs to multiple communities. Therefore, overlapping community detection is an important task in social network analysis. CDFR is an efficient algorithm recently proposed for non-overlapping community detection. In this paper, CDFR is extended for overlapping community detection, and the extended algorithm is called as OCDFR. In OCDFR, CDFR is first called to obtain a non-overlapping community partition. Then, find the kth NGC (k=1, 2, 3,..) node for each node, and record the fuzzy relations between them. Based on the fuzzy relation between a node and its kth NGC, five decision methods are proposed to determine whether it can join the communities to which its kth NGC belongs. Experimental results on real-world networks and synthetic networks demonstrate that OCDFR is effective and highly competitive.

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http://hdl.handle.net/10453/133854