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In the past few years, graph neural networks (GNNs) have become the de facto model of choice for graph classification.
Fast graph representation learning with pytorch geometric, 2019
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R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
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M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2019
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G. Bravo-Hermsdorff and L. M. Gunderson · 2019
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Fully dynamic spectral vertex sparsifiers and applications
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Pytorch lightning
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On the equivalence of molecular graph convolution and molecular wave function with poor basis set
M. Tsubaki and T. Mizoguchi · 2020
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A survey of unsupervised deep domain adaptation
G. Wilson and D. J. Cook · 2020
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A comprehensive survey on graph neural networks
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Size-invariant graph representations for graph classification extrapolations
B. Bevilacqua, Y. Zhou, and B. Ribeiro · 2021
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Graph coarsening with neural networks
C. Cai, D. Wang, and Y. Wang · 2021
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Combinatorial optimization and reasoning with graph neural networks
Q. Cappart, D. Chételat, E. B. Khalil, A. Lodi, C. Morris, and P. Veličković · 2021
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Exact Combinatorial Optimization with Graph Convolutional Neural Networks
M. Gasse, D. Chételat, N. Ferroni, L. Charlin, and A. Lodi · 2019
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Similarity of neural network representations revisited
S. Kornblith, M. Norouzi, H. Lee, and G. Hinton · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe · 2019
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Relational pooling for graph representations
R. Murphy, B. Srinivasan, V. Rao, and B. Ribeiro · 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, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Learning to solve NP-complete problems: A graph neural network for decision TSP
M. Prates, P. H. C. Avelar, H. Lemos, L. C. Lamb, and M. Y. Vardi · 2019
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A closer look at distribution shifts and out-of-distribution generalization on graphs
M. Ding, K. Kong, J. Chen, J. Kirchenbauer, M. Goldblum, D. Wipf, F. Huang, and T. Goldstein · 2021
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Generalize a small pre-trained model to arbitrarily large tsp instances
Z.-H. Fu, K.-B. Qiu, and H. Zha · 2021
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Benchmarking graph neural networks for materials chemistry
V. Fung, J. Zhang, E. Juarez, and B. G. Sumpter · 2021
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Domain adaptation for medical image analysis: a survey
H. Guan and M. Liu · 2021
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Learning TSP Requires Rethinking Generalization
C. K. Joshi, Q. Cappart, L.-M. Rousseau, and T. Laurent · 2021
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Gemnet: Universal directional graph neural networks for molecules
J. Klicpera, F. Becker, and S. Günnemann · 2021
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Unsupervised learning of graph hierarchical abstractions with differentiable coarsening and optimal transport
T. Ma and J. Chen · 2021
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The risks of invariant risk minimization
E. Rosenfeld, P. Ravikumar, and A. Risteski · 2021
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Qa-gnn: Reasoning with language models and knowledge graphs for question answering
M. Yasunaga, H. Ren, A. Bosselut, P. Liang, and J. Leskovec · 2021
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From local structures to size generalization in graph neural networks
G. Yehudai, E. Fetaya, E. Meirom, G. Chechik, and H. Maron · 2021
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Scaling up graph neural networks via graph coarsening
C. X. T. L. Zengfeng Huang, Shengzhong Zhang and M. Zhou · 2021
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Shift-robust gnns: Overcoming the limitations of localized graph training data
Q. Zhu, N. Ponomareva, J. Han, and B. Perozzi · 2021
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Graph representation learning for multi-task settings: a meta-learning approach
D. Buffelli and F. Vandin · 2022
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Graph coarsening: from scientific computing to machine learning
J. Chen, Y. Saad, and Z. Zhang · 2022
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How do graph networks generalize to large and diverse molecular systems?, 2022
J. Gasteiger, M. Shuaibi, A. Sriram, S. Günnemann, Z. Ulissi, C. L. Zitnick, and A. Das · 2022
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Graph condensation for graph neural networks
W. Jin, L. Zhao, S. Zhang, Y. Liu, J. Tang, and N. Shah · 2022
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