Bridging and Compressing Feature and Semantic Spaces for Robust Graph Neural Networks: An Information Theory Perspective-Reference-Cited by-同舟云学术

Bridging and Compressing Feature and Semantic Spaces for Robust Graph Neural Networks: An Information Theory Perspective

Published:2024-08-24 Issue: Volume: Page:4571-4582
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Zhong Luying¹^ORCID,Lin Renjie¹^ORCID,Li Jiayin²^ORCID,Wang Shiping¹^ORCID,Chen Zheyi¹^ORCID

Affiliation:

1. Fuzhou University, Fuzhou, China

2. Fujian Normal University, Fuzhou, China

Funder

National Natural Science Foundation of China

Funds for Scientific Research of Fujian Provincial Department of Finance

Central Funds Guiding the Local Science and Technology Development

Fujian Province Technology and Economy Integration Service Platform

Fuzhou-Xiamen-Quanzhou National Independent Innovation Demonstration Zone Collaborative Innovation Platform

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671870

Reference44 articles.

1. Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. 2017. Deep Variational Information Bottleneck. In Proceedings of the International Conference on Learning Representations, ICLR. 1--19.

2. Ishmael Belghazi, Sai Rajeswar, Aristide Baratin, R. Devon Hjelm, and Aaron C. Courville. 2018. MINE: Mutual Information Neural Estimation. CoRR, Vol. arXiv:1801.04062v5 (2018).

3. Yu Chen, Lingfei Wu, and Mohammed Zaki. 2020. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. Advances in Neural Information Processing Systems, NIPS (2020), 19314--19326.

4. Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, and Lawrence Carin. 2020. Club: A contrastive log-ratio upper bound of mutual information. In Proceedings of the International Conference on Machine Learning, ICML. 1779--1788.

5. A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy