Alleviating over-smoothing via graph sparsification based on vertex feature similarity

Author:

Wu Gongce,Lin Shukuan,Zhuang Yilin,Qiao Jianzhong

Funder

Science Research Project of Liaoning Department of Education of China

Science and Technology Development Project of Liaoning Province of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference41 articles.

1. Batson JD, Spielman DA, Srivastava N et al (2013) Spectral sparsification of graphs: theory and algorithms. Commun ACM 56(8):87–94. https://doi.org/10.1145/2492007.2492029

2. Bruna J, Zaremba W, Szlam A et al (2014) Spectral networks and locally connected networks on graphs. In: 2nd international conference on learning representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings

3. Cai C, Wang Y (2020) A note on over-smoothing for graph neural networks. arXiv:2006.13318 [cs.LG]

4. Chen J, Ma T, Xiao C (2018) Fastgcn: fast learning with graph convolutional networks via importance sampling. In: 6th international conference on learning representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings

5. Chen M, Wei Z, Huang Z et al (2020) Simple and deep graph convolutional networks. In: Proceedings of the 37th international conference on machine learning, ICML 2020, 13-18 July 2020, Virtual Event, Proceedings of Machine Learning Research, vol 119. PMLR, pp 1725–1735

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