An Evolutionary Approach Based on Symmetric Nonnegative Matrix Factorization for Community Detection in Dynamic Networks-Reference-Cited by-同舟云学术

An Evolutionary Approach Based on Symmetric Nonnegative Matrix Factorization for Community Detection in Dynamic Networks

Published:2019-12-01 Issue:12 Volume:E102.D Page:2619-2623
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

PAN Yu¹,HU Guyu¹,PAN Zhisong¹,WANG Shuaihui¹,SHAO Dongsheng¹

Affiliation:

1. Institute of Command and Control Engineering, Army Engineering University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E102.D/12/E102.D_2019EDL8046/_pdf

Reference10 articles.

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2. [2] Y.-R. Lin, Y. Chi, S. Zhu, H. Sundaram, and B.L. Tseng, “Analyzing communities and their evolutions in dynamic social networks,” ACM Transactions on Knowledge Discovery from Data, vol.3, no.2, pp.1-31, 2009. 10.1145/1514888.1514891

3. [3] F. Folino and C. Pizzuti, “An Evolutionary Multiobjective Approach for Community Discovery in Dynamic Networks,” IEEE Trans. Knowl. Data Eng., vol.26, no.8, pp.1838-1852, 2014. 10.1109/tkde.2013.131

4. [4] X. Ma and D. Di, “Evolutionary nonnegative matrix factorization algorithms for community detection in dynamic networks,” IEEE Trans. Knowl. Data Eng., vol.29, no.5, pp.1045-1058, 2017.

5. [5] P. Jiao, et al., “Exploring temporal community structure and constant evolutionary pattern hiding in dynamic networks,” Neurocomputing, vol.3, no.14, pp.224-233, 2018.

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