The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs-Reference-Cited by-同舟云学术

The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs

Published:2024-08-24 Issue: Volume:34 Page:3164-3175
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Kun¹^ORCID,Zhang Guibin²^ORCID,Zhang Xinnan³^ORCID,Fang Junfeng¹^ORCID,Wu Xun⁴^ORCID,Li Guohao⁵^ORCID,Pan Shirui⁶^ORCID,Huang Wei⁷^ORCID,Liang Yuxuan⁸^ORCID

Affiliation:

1. University of Science and Technology of China (USTC), Hefei, China

2. Tongji University, Shanghai, China

3. University of Minnesota, Twin Cities, MN, USA

4. Tsinghua University, Beijing, China

5. Oxford University, Oxford, United Kingdom

6. Griffith University, Queensland, Australia

7. RIKEN AIP, Tokyo, Japan

8. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Funder

Guangzhou-HKUST(GZ) Joint Funding Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671791

Reference70 articles.

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2. Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In international conference on machine learning. PMLR, 21--29.

3. Anonymous. 2023. Graph Lottery Ticket Automated. In Submitted to The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=nmBjBZoySX under review.

4. Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View