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Graphs with heterophily have been regarded as challenging scenarios for Graph Neural Networks (GNNs), where nodes are connected with dissimilar neighbors through various patterns.
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Adam: A method for stochastic optimization
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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W. W., and Salakhutdinov, R · 2016
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Neural message passing for quantum chemistry
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, Z., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Contextual stochastic block models
Deshpande, Y., Sen, S., Montanari, A., and Mossel, E · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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High-dimensional probability: An introduction with applications in data science , volume 47
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Simplifying graph convolutional networks
Wu, F., Jr., A. H. S., Zhang, T., Fifty, C., Yu, T., and Weinberger, K. Q · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T · 2020
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Geom-gcn: Geometric graph convolutional networks
Pei, H., Wei, B., Chang, K. C.-C., Lei, Y., and Yang, B · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., and Koutra, D · 2020
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Graph convolution for semi-supervised classification: Improved linear separability and out-of-distribution generalization
Baranwal, A., Fountoulakis, K., and Jagannath, A · 2021
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Beyond low-frequency information in graph convolutional networks
Revisiting heterophily for graph neural networks
Luan, S., Hua, C., Lu, Q., Zhu, J., Zhao, M., Zhang, S., Chang, X.-W., and Precup, D · 2022
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Understanding non-linearity in graph neural networks from the bayesian-inference perspective
Wei, R., Yin, H., Jia, J., Benson, A. R., and Li, P · 2022
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
Yan, Y., Hashemi, M., Swersky, K., Yang, Y., and Koutra, D · 2022
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Effects of graph convolutions in multi-layer networks
Baranwal, A., Fountoulakis, K., and Jagannath, A · 2023
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Learnable graph convolutional attention networks
Javaloy, A., Sanchez-Martin, P., Levi, A., and Valera, I · 2023
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When do graph neural networks help with node classification: Investigating the homophily principle on node distinguishability
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Bo, D., Wang, X., Shi, C., and Shen, H · 2021
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Adaptive universal generalized pagerank graph neural network
Chien, E., Peng, J., Li, P., and Milenkovic, O · 2021
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Universal graph convolutional networks
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On the universality of graph neural networks on large random graphs
Keriven, N., Bietti, A., and Vaiter, S · 2021
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New benchmarks for learning on non-homophilous graphs
Lim, D., Li, X., Hohne, F., and Lim, S.-N · 2021
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Bi-gcn: Binary graph convolutional network
Wang, J., Wang, Y., Yang, Z., Yang, L., and Guo, Y · 2021
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Finding global homophily in graph neural networks when meeting heterophily
Li, X., Zhu, R., Cheng, Y., Shan, C., Luo, S., Li, D., and Qian, W · 2022
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Luan, S., Hua, C., Xu, M., Lu, Q., Zhu, J., Chang, X.-W., Fu, J., Leskovec, J., and Precup, D · 2023
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Demystifying structural disparity in graph neural networks: Can one size fit all?
Mao, H., Chen, Z., Jin, W., Han, H., Ma, Y., Zhao, T., Shah, N., and Tang, J · 2023
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A critical look at the evaluation of gnns under heterophily: are we really making progress?
Platonov, O., Kuznedelev, D., Diskin, M., Babenko, A., and Prokhorenkova, L · 2023
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Ordered gnn: Ordering message passing to deal with heterophily and over-smoothing
Song, Y., Zhou, C., Wang, X., and Lin, Z · 2023
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Heterophily-aware graph attention network
Wang, J., Guo, Y., Yang, L., and Wang, Y · 2023
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A non-asymptotic analysis of oversmoothing in graph neural networks
Wu, X., Chen, Z., Wang, W., and Jadbabaie, A · 2023
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Finding the missing-half: Graph complementary learning for homophily-prone and heterophily-prone graphs
Zheng, Y., Zhang, H., Lee, V., Zheng, Y., Wang, X., and Pan, S · 2023
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