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Homophily is a graph property describing the tendency of edges to connect similar nodes; the opposite is called heterophily.
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A. Lancichinetti, S. Fortunato, and F. Radicchi · 2008
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Adam: A method for stochastic optimization
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Gaussian error linear units (GELUs)
D. Hendrycks and K. Gimpel · 2016
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Equivalence between modularity optimization and maximum likelihood methods for community detection
M. E. Newman · 2016
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Revisiting semi-supervised learning with graph embeddings
Z. Yang, W. Cohen, and R. Salakhudinov · 2016
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Masked label prediction: Unified message passing model for semi-supervised classification
Y. Shi, Z. Huang, S. Feng, H. Zhong, W. Wang, and Y. Sun · 2020
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GraphSAINT: Graph sampling based inductive learning method
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra · 2020
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Adaptive universal generalized pagerank graph neural network
E. Chien, J. Peng, P. Li, and O. Milenkovic · 2021
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Expertise and dynamics within crowdsourced musical knowledge curation: A case study of the genius platform
D. Lim and A. R. Benson · 2021
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Pitfalls of graph neural network evaluation
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann · 2018
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Graph Attention Networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
S. Abu-El-Haija, B. Perozzi, A. Kapoor, N. Alipourfard, K. Lerman, H. Harutyunyan, G. Ver Steeg, and A. Galstyan · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
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Community detection through likelihood optimization: in search of a sound model
L. Prokhorenkova and A. Tikhonov · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
M. Wang, D. Zheng, Z. Ye, Q. Gan, M. Li, X. Song, J. Zhou, C. Ma, L. Yu, Y. Gai, et al · 2019
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
D. Lim, F. Hohne, X. Li, S. L. Huang, V. Gupta, O. Bhalerao, and S. N. Lim · 2021
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B. Rozemberczki and R. Sarkar · 2021
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Multi-scale attributed node embedding
B. Rozemberczki, C. Allen, and R. Sarkar · 2021
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Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns
S. Suresh, V. Budde, J. Neville, P. Li, and J. Ma · 2021
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Graph neural networks with heterophily
J. Zhu, R. A. Rossi, A. Rao, T. Mai, N. Lipka, N. K. Ahmed, and D. Koutra · 2021
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Taxonomy of benchmarks in graph representation learning
R. Liu, S. Cantürk, F. Wenkel, S. McGuire, X. Wang, A. Little, L. O’Bray, M. Perlmutter, B. Rieck, M. Hirn, et al · 2022
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Revisiting heterophily for graph neural networks
S. Luan, C. Hua, Q. Lu, J. Zhu, M. Zhao, S. Zhang, X.-W. Chang, and D. Precup · 2022
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Is homophily a necessity for graph neural networks?
Y. Ma, X. Liu, N. Shah, and J. Tang · 2022
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S. Luan, C. Hua, M. Xu, Q. Lu, J. Zhu, X.-W. Chang, J. Fu, J. Leskovec, and D. Precup · 2023
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A critical look at the evaluation of gnns under heterophily: Are we really making progress?
O. Platonov, D. Kuznedelev, M. Diskin, A. Babenko, and L. Prokhorenkova · 2023
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