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Evolutionary Nonnegative Matrix Factorization Algorithms for Community Detection in Dynamic Networks
Ma, Xiaoke1; Dong, Di2
2017-05-01
发表期刊IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷号29期号:5页码:1045-1058
文章类型Article
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.
关键词Dynamic Networks Community Structure Nonnegative Matrix Factorization Evolutionary Clustering
WOS标题词Science & Technology ; Technology
DOI10.1109/TKDE.2017.2657752
关键词[WOS]COMPLEX NETWORKS ; BIG DATA ; GRAPHS
收录类别SCI
语种英语
项目资助者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)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号WOS:000399289300009
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15094
专题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
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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.
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