EXGC: Bridging Efficiency and Explainability in Graph Condensation

Author:

Fang Junfeng1ORCID,Li Xinglin2ORCID,Sui Yongduo3ORCID,Gao Yuan3ORCID,Zhang Guibin4ORCID,Wang Kun3ORCID,Wang Xiang3ORCID,He Xiangnan3ORCID

Affiliation:

1. University of Science and Technology of China, Hefei, Anhui, China

2. Hunan University, Hefei, China

3. University of Science and Technology of China, Hefei, China

4. Tongji University, Shanghai, China

Funder

the National Natural Science Foundation of China

National Natural Science Foundation of China

the CCCD Key Lab of Ministry of Culture and Tourism

Publisher

ACM

Reference102 articles.

1. Anonymous. 2024 a. Graph Lottery Ticket Automated. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=nmBjBZoySX

2. Anonymous. 2024 b. NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=sLdVl0q68X

3. Federico Baldassarre and Hossein Azizpour. 2019. Explainability Techniques for Graph Convolutional Networks. CoRR, Vol. abs/1905.13686 (2019).

4. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples;Belkin Mikhail;Journal of machine learning research,2006

5. Christopher M Bishop and Nasser M Nasrabadi. 2006. Pattern recognition and machine learning. Vol. 4. Springer.

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