Comparing local modularity optimization for detecting communities in networks

doi:10.1142/S012918311750084X

	Comparing local modularity optimization for detecting communities in networks
	Xiang, Ju 1; Wang, Zhi-Zhong 2; Li, Hui-Jia 3,4; Zhang, Yan 5; Chen, Shi 5; Liu, Cui-Cui 5; Li, Jian-Ming 1; Guo, Li-Juan 6
刊名	INTERNATIONAL JOURNAL OF MODERN PHYSICS C
	2017-06-01
卷号	28 期号:6 页码:11
关键词	Community structure community detection complex networks
ISSN号	0129-1831
DOI	10.1142/S012918311750084X
英文摘要	Community detection is one important problem in network theory, and many methods have been proposed for detecting community structures in the networks. Given quality functions for evaluating community structures, community detection can be considered as one kind of optimization problem, such as modularity optimization, therefore, optimization of quality functions has been one of the most popular strategies for community detection. In this paper, we introduced two kinds of local modularity functions for community detection, and the self consistent method is introduced to optimize the local modularity for detecting communities in the networks. We analyze the behaviors of the modularity optimizations, and compare the performance of them in community detection. The results confirm the superiority of the local modularity in detecting community structures, especially on large-size and heterogeneous networks.
资助项目	construct program of the key discipline in Hunan province ; Scientific Research Fund of Education Department of Hunan Province[17A024] ; Scientific Research Fund of Education Department of Hunan Province[17C0180] ; Scientific Research Fund of Education Department of Hunan Province[17B034] ; Scientific Research Fund of Education Department of Hunan Province[14C0112] ; Scientific Research Fund of Education Department of Hunan Province[15C0164] ; Scientific Research Fund of Education Department of Hunan Province[14B024] ; Scientific Research Project of Hunan Provincial Health and Family Planning Commission of China[C2017013] ; Project of Changsha Medical University[KY201517] ; Department of Education of Hunan Province[15A023] ; Hunan Provincial Natural Science Foundation of China[2015JJ6010] ; Hunan Provincial Natural Science Foundation of China[13JJ4045] ; National Natural Science Foundation of China[11404178] ; National Natural Science Foundation of China[71401194]
WOS研究方向	Computer Science ; Physics
语种	英语
出版者	WORLD SCIENTIFIC PUBL CO PTE LTD
WOS记录号	WOS:000404056100014
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/25775]
专题	中国科学院数学与系统科学研究院
通讯作者	Li, Hui-Jia; Li, Jian-Ming; Guo, Li-Juan
作者单位	1.Changsha Med Univ, Neurosci Res Ctr, Changsha 410219, Hunan, Peoples R China 2.Hunan First Normal Univ, South City Coll, Changsha 410205, Hunan, Peoples R China 3.Cent Univ Finance & Econ, Sch Management Sci & Engn, Beijing 100080, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 5.Changsha Med Univ, Dept Comp Sci, Changsha 410219, Hunan, Peoples R China 6.Changsha Med Univ, Dept Basic Med Sci, Changsha 410219, Hunan, Peoples R China
推荐引用方式 GB/T 7714	Xiang, Ju,Wang, Zhi-Zhong,Li, Hui-Jia,et al. Comparing local modularity optimization for detecting communities in networks[J]. INTERNATIONAL JOURNAL OF MODERN PHYSICS C,2017,28(6):11.
APA	Xiang, Ju.,Wang, Zhi-Zhong.,Li, Hui-Jia.,Zhang, Yan.,Chen, Shi.,...&Guo, Li-Juan.(2017).Comparing local modularity optimization for detecting communities in networks.INTERNATIONAL JOURNAL OF MODERN PHYSICS C,28(6),11.
MLA	Xiang, Ju,et al."Comparing local modularity optimization for detecting communities in networks".INTERNATIONAL JOURNAL OF MODERN PHYSICS C 28.6(2017):11.