Deep Multi-Graph Clustering via Attentive Cross-Graph Association-Reference-Cited by-同舟云学术

Deep Multi-Graph Clustering via Attentive Cross-Graph Association

Published:2020-01-20 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 13th International Conference on Web Search and Data Mining
language:
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Author:

Luo Dongsheng¹,Ni Jingchao²,Wang Suhang¹,Bian Yuchen³,Yu Xiong⁴,Zhang Xiang¹

Affiliation:

1. Pennsylvania State University, State College, PA, USA

2. NEC Laboratories America, Princeton, NJ, USA

3. Baidu Research, Sunnyvale, CA, USA

4. Case Western Reserve University, Cleveland, OH, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3336191.3371806

Reference37 articles.

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2. Wei Cheng Xiang Zhang Zhishan Guo Yubao Wu Patrick F Sullivan and Wei Wang. 2013. Flexible and robust co-regularized multi-domain graph clustering. In SIGKDD . Wei Cheng Xiang Zhang Zhishan Guo Yubao Wu Patrick F Sullivan and Wei Wang. 2013. Flexible and robust co-regularized multi-domain graph clustering. In SIGKDD .

3. Gary William Flake Robert E Tarjan and Kostas Tsioutsiouliklis. 2004. Graph clustering and minimum cut trees. Internet Mathematics (2004). Gary William Flake Robert E Tarjan and Kostas Tsioutsiouliklis. 2004. Graph clustering and minimum cut trees. Internet Mathematics (2004).

4. Mahsa Ghorbani Mahdieh Soleymani Baghshah and Hamid R. Rabiee. 2018. Multi-layered Graph Embedding with Graph Convolutional Networks. arXiv preprint arXiv:1811.08800 (2018). Mahsa Ghorbani Mahdieh Soleymani Baghshah and Hamid R. Rabiee. 2018. Multi-layered Graph Embedding with Graph Convolutional Networks. arXiv preprint arXiv:1811.08800 (2018).

5. Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS . Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS .

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