Evolutionary Nonnegative Matrix Factorization Algorithms for Community Detection in Dynamic Networks

doi:10.1109/TKDE.2017.2657752

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Evolutionary Nonnegative Matrix Factorization Algorithms for Community Detection in Dynamic Networks
	Ma, Xiaoke 1; Dong, Di2
刊名	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2017-05-01
卷号	29 期号:5 页码:1045-1058
关键词	Dynamic Networks Community Structure Nonnegative Matrix Factorization Evolutionary Clustering
DOI	10.1109/TKDE.2017.2657752
文献子类	Article
英文摘要	Discovering evolving communities in dynamic networks is essential to important applications such as analysis for dynamic web content and disease progression. Evolutionary clustering uses the temporal smoothness framework that simultaneously maximizes the clustering accuracy at the current time step and minimizes the clustering drift between two successive time steps. In this paper, we propose two evolutionary nonnegative matrix factorization (ENMF) frameworks for detecting dynamic communities. To address the theoretical relationship among evolutionary clustering algorithms, we first prove the equivalence relationship between ENMF and optimization of evolutionary modularity density. Then, we extend the theory by proving the equivalence between evolutionary spectral clustering and ENMF, which serves as the theoretical foundation for hybrid algorithms. Based on the equivalence, we propose a semi-supervised ENMF (sE-NMF) by incorporating a priori information into ENMF. Unlike the traditional semi-supervised algorithms, a priori information is integrated into the objective function of the algorithm. The main advantage of the proposed algorithm is to escape the local optimal solution without increasing time complexity. The experimental results over a number of artificial and real world dynamic networks illustrate that the proposed method is not only more accurate but also more robust than the state-of-the-art approaches.
WOS关键词	COMPLEX NETWORKS ; BIG DATA ; GRAPHS
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000399289300009
资助机构	NSFC(61502363 ; Natural Science Funding of Shaanxi Province(2016JQ6044) ; Fundamental Research Funding of Central Universities(JB160306) ; Natural Science Basic Research Plan in Ningbo City(2016A610034) ; 81271569)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/15094]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Ma, Xiaoke
作者单位	1.Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Shaanxi, Peoples R China 2.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ma, Xiaoke,Dong, Di. Evolutionary Nonnegative Matrix Factorization Algorithms for Community Detection in Dynamic Networks[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2017,29(5):1045-1058.
APA	Ma, Xiaoke,&Dong, Di.(2017).Evolutionary Nonnegative Matrix Factorization Algorithms for Community Detection in Dynamic Networks.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,29(5),1045-1058.
MLA	Ma, Xiaoke,et al."Evolutionary Nonnegative Matrix Factorization Algorithms for Community Detection in Dynamic Networks".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 29.5(2017):1045-1058.