Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification

Author:

Choi Yoonhyuk1,Choi Jiho2,Ko Taewook2,Byun Hyungho2,Kim Chong-Kwon3

Affiliation:

1. Seoul National University, Seoul, South Korea

2. Seoul National University, Seoul, Republic of Korea

3. Korea Institute of Energy Technology, Naju, Republic of Korea

Funder

Innovative Human Resource Development for Local Intellectualization support program

National Research Foundation of Korea (NRF)

Artificial Intelligence Innovation Hub

Publisher

ACM

Reference53 articles.

1. Deyu Bo , Xiao Wang , Chuan Shi , and Huawei Shen . 2021. Beyond low-frequency information in graph convolutional networks. arXiv preprint arXiv:2101.00797 ( 2021 ). Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen. 2021. Beyond low-frequency information in graph convolutional networks. arXiv preprint arXiv:2101.00797 (2021).

2. Neural Graph Learning

3. Ming Chen , Zhewei Wei , Zengfeng Huang , Bolin Ding , and Yaliang Li . 2020 . Simple and deep graph convolutional networks . In International Conference on Machine Learning. PMLR, 1725--1735 . Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and deep graph convolutional networks. In International Conference on Machine Learning. PMLR, 1725--1735.

4. Marco Cuturi and Arnaud Doucet . 2014 . Fast computation of Wasserstein barycenters . In International conference on machine learning. PMLR, 685--693 . Marco Cuturi and Arnaud Doucet. 2014. Fast computation of Wasserstein barycenters. In International conference on machine learning. PMLR, 685--693.

5. Michaël Defferrard , Xavier Bresson , and Pierre Vandergheynst . 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems , Vol. 29 ( 2016 ). Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, Vol. 29 (2016).

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3. Causality and Independence Enhancement for Biased Node Classification;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

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