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Graph Neural Networks often struggle with long-range information propagation and in the presence of heterophilous neighborhoods.
Diagonal equivalence to matrices with prescribed row and column sums
Richard Sinkhorn · 1967
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Concerning nonnegative matrices and doubly stochastic matrices
Richard Sinkhorn and Paul Knopp · 1967
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Matrix links, an extremization problem, and the reduction of a non-negative matrix to one with prescribed row and column sums
MV Menon · 1968
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On the scaling of multidimensional matrices
Joel Franklin and Jens Lorenz · 1989
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The rate of convergence of sinkhorn balancing
George W Soules · 1991
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Optimal transport: old and new
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Jiong Zhu, Ryan A Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K Ahmed, and Danai Koutra · 2009
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Spectral networks and locally connected networks on graphs
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Sinkhorn distances: Lightspeed computation of optimal transport
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William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
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Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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Inference in probabilistic graphical models by graph neural networks
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, and Xaq Pitkow · 2019
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Chen Cai and Yusu Wang · 2020
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Understanding and resolving performance degradation in deep graph convolutional networks
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Graph neural networks with heterophily
Jiong Zhu, Ryan A Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K Ahmed, and Danai Koutra · 2021
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Diffwire: Inductive graph rewiring via the lovász bound
Adrián Arnaiz-Rodríguez, Ahmed Begga, Francisco Escolano, and Nuria M Oliver · 2022
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Oversquashing in gnns through the lens of information contraction and graph expansion
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Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily
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Adaptive universal generalized pagerank graph neural network
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Residual correlation in graph neural network regression
Junteng Jia and Austion R Benson · 2020
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p-laplacian based graph neural networks
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Gps++: An optimised hybrid mpnn/transformer for molecular property prediction
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Understanding oversquashing in gnns through the lens of effective resistance
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Attending to graph transformers
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A critical look at the evaluation of gnns under heterophily: are we really making progress?
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A survey on oversmoothing in graph neural networks
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End-to-end differentiable clustering with associative memories
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Graph clustering with graph neural networks
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Factor graph neural networks
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Differentiable clustering with perturbed spanning forests
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