Detecting Communities and Correlated Attribute Clusters on Multi-Attributed Graphs-Reference-Cited by-同舟云学术

Detecting Communities and Correlated Attribute Clusters on Multi-Attributed Graphs

Published:2019-04-01 Issue:4 Volume:E102.D Page:810-820
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

ITO Hiroyoshi¹,KOMAMIZU Takahiro²,AMAGASA Toshiyuki³,KITAGAWA Hiroyuki³

Affiliation:

1. Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba

2. Information Technology Center, Nagoya University

3. Center for Computational Sciences, University of Tsukuba

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/4/E102.D_2018DAP0022/_pdf

Reference30 articles.

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3. [3] M. Frank, A.P. Streich, D. Basin, and J.M. Buhmann, “Multi-assignment clustering for boolean data,” Journal of Machine Learning Research, vol.13, no.Feb, pp.459-489, 2012.

4. [4] M. Girvan and M.E.J. Newman, “Community structure in social and biological networks,” Proc. National Academy of Sciences of the United States of America, vol.99, no.12, pp.7821-7826, 2002. 10.1073/pnas.122653799

5. [5] J. Yang and J. Leskovec, “Overlapping community detection at scale: A nonnegative matrix factorization approach,” Proc. Sixth ACM International Conference on Web Search and Data Mining, pp.587-596, ACM, 2013. 10.1145/2433396.2433471

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