CASIA OpenIR  > 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, Xiaoke1; Dong, Di2
Source PublicationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
2017-05-01
Volume29Issue:5Pages:1045-1058
SubtypeArticle
AbstractDiscovering 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.
KeywordDynamic Networks Community Structure Nonnegative Matrix Factorization Evolutionary Clustering
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TKDE.2017.2657752
WOS KeywordCOMPLEX NETWORKS ; BIG DATA ; GRAPHS
Indexed BySCI
Language英语
Funding OrganizationNSFC(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 Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000399289300009
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15094
Collection中国科学院分子影像重点实验室
Corresponding AuthorMa, Xiaoke
Affiliation1.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
Recommended Citation
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.
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