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We investigate graph neural networks on graphs with heterophily.
Inverting modified matrices
Max, A. W · 1950
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Birds of a feather: Homophily in social networks
McPherson, M., Smith-Lovin, L., and Cook, J. M · 2001
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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 · 2006
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Social influence analysis in large-scale networks
Tang, J., Sun, J., Wang, C., and Yang, Z · 2009
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Graph neural networks with heterophily
Zhu, J., Rossi, R. A., Rao, A., Mai, T., Lipka, N., Ahmed, N. K., and Koutra, D · 2009
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Robust recovery of subspace structures by low-rank representation
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., and Ma, Y · 2012
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Robust and efficient subspace segmentation via least squares regression
Lu, C.-Y., Min, H., Zhao, Z.-Q., Zhu, L., Huang, D.-S., and Yan, S · 2012
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Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2013
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Political homophily and collaboration in regional planning networks
Gerber, E. R., Henry, A. D., and Lubell, M · 2013
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Homophily and missing links in citation networks
Ciotti, V., Bonaventura, M., Nicosia, V., Panzarasa, P., and Latora, V · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Adversarial attack on graph structured data
Dai, H., Li, H., Tian, T., Huang, X., Wang, L., Zhu, J., and Song, L · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J., Bojchevski, A., and Günnemann, S · 2018
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Ver Steeg, G., and Galstyan, A · 2019
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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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Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
Vu, M. N. and Thai, M. T · 2020
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Beyond low-frequency information in graph convolutional networks
Bo, D., Wang, X., Shi, C., and Shen, H · 2021
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Graph neural networks with adaptive frequency response filter
Dong, Y., Ding, K., Jalaian, B., Ji, S., and Li, J · 2021
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
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Rong, Y., Huang, W., Xu, T., and Huang, J · 2019
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Pairnorm: Tackling oversmoothing in gnns
Zhao, L. and Akoglu, L · 2019
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Robust graph convolutional networks against adversarial attacks
Zhu, D., Zhang, Z., Cui, P., and Zhu, W · 2019
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Simple and deep graph convolutional networks
Chen, M., Wei, Z., Huang, Z., Ding, B., and Li, Y · 2020
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Adaptive universal generalized pagerank graph neural network
Chien, E., Peng, J., Li, P., and Milenkovic, O · 2020
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Cast: A correlation-based adaptive spectral clustering algorithm on multi-scale data
Li, X., Kao, B., Shan, C., Yin, D., and Ester, M · 2020
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Towards deeper graph neural networks
Liu, M., Gao, H., and Ji, S · 2020
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Lim, D., Hohne, F., Li, X., Huang, S. L., Gupta, V., Bhalerao, O., and Lim, S. N · 2021
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Non-local graph neural networks
Liu, M., Wang, Z., and Ji, S · 2021
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Is heterophily a real nightmare for graph neural networks to do node classification?
Luan, S., Hua, C., Lu, Q., Zhu, J., Zhao, M., Zhang, S., Chang, X.-W., and Precup, D · 2021
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Reinforcement learning enhanced explainer for graph neural networks
Shan, C., Shen, Y., Zhang, Y., Li, X., and Li, D · 2021
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Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns
Suresh, S., Budde, V., Neville, J., Li, P., and Ma, J · 2021
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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 · 2021
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Graph neural networks inspired by classical iterative algorithms
Yang, Y., Liu, T., Wang, Y., Zhou, J., Gan, Q., Wei, Z., Zhang, Z., Huang, Z., and Wipf, D · 2021
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