Cooperative Classification and Rationalization for Graph Generalization

Author:

Yue Linan1ORCID,Liu Qi1ORCID,Liu Ye1ORCID,Gao Weibo1ORCID,Yao Fangzhou1ORCID,Li Wenfeng2ORCID

Affiliation:

1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China

2. Douyin Vision Co., Ltd, Beijing, China

Funder

National Key Research and Development Program of China

Publisher

ACM

Reference50 articles.

1. Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. 2017. Deep Variational Information Bottleneck. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings.

2. Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019).

3. Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018. Mutual information neural estimation. In International Conference on Machine Learning. PMLR, 531--540.

4. Beatrice Bevilacqua, Yangze Zhou, and Bruno Ribeiro. 2021. Size-invariant graph representations for graph classification extrapolations. In International Conference on Machine Learning. PMLR, 837--851.

5. Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks;Buffelli Davide;Advances in Neural Information Processing Systems,2022

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