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In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN).
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Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs. In 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 . Springer Science and Business Media Deutschland GmbH, 541–553
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks. In 2022 IEEE International Conference on Data Mining (ICDM) . IEEE, 1287–1292
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Learning from Counterfactual Links for Link Prediction. In International Conference on Machine Learning . PMLR, 26911–26926
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Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
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From canonical correlation analysis to self-supervised graph neural networks. In Thirty-Fifth Conference on Neural Information Processing Systems
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Label-Wise Graph Convolutional Network for Heterophilic Graphs. In Learning on Graphs Conference . PMLR, 1–26
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Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, and Jian Tang. 2022 · 2022
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Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks
Runlin Lei, Zhen Wang, Yaliang Li, Bolin Ding, and Zhewei Wei. 2022 · 2022
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Orphicx: A causality-inspired latent variable model for interpreting graph neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 13729–13738
Wanyu Lin, Hao Lan, Hao Wang, and Baochun Li. 2022 · 2022
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Revisiting heterophily for graph neural networks
Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup. 2022 · 2022
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Gppt: Graph pre-training and prompt tuning to generalize graph neural networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1717–1727
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Knowledge Graph Completion with Counterfactual Augmentation. In Proceedings of the ACM Web Conference 2023 . 2611–2620
Heng Chang, Jie Cai, and Jia Li. 2023 · 2023
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Wiener graph deconvolutional network improves graph self-supervised learning. In Proceedings of the AAAI conference on artificial intelligence , Vol. 37. 7131–7139
Jiashun Cheng, Man Li, Jia Li, and Fugee Tsung. 2023 · 2023
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Causal Lifting and Link Prediction
Leonardo Cotta, Beatrice Bevilacqua, Nesreen Ahmed, and Bruno Ribeiro. 2023 · 2023
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Linkless link prediction via relational distillation. In International Conference on Machine Learning . PMLR, 12012–12033
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GOAT: A Global Transformer on Large-scale Graphs. In Proceedings of the 40th International Conference on Machine Learning
Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C Bayan Bruss, and Tom Goldstein. 2023 · 2023
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Graph Prompt Learning: A Comprehensive Survey and Beyond
Xiangguo Sun, Jiawen Zhang, Xixi Wu, Hong Cheng, Yun Xiong, and Jia Li. 2023b · 2023
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Deep Insights into Noisy Pseudo Labeling on Graph Data. In Thirty-seventh Conference on Neural Information Processing Systems
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Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
Yizhen Zheng, He Zhang, Vincent Lee, Yu Zheng, Xiao Wang, and Shirui Pan. 2023 · 2023
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