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Characterizing the separation power of graph neural networks (GNNs) provides an understanding of their limitations for graph learning tasks.
Graded modal logic and counting bisimulation
Martin Otto · 1910
Earlier work this paper cites.
The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
H. L. Morgan · 1965
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Fast parallel matrix inversion algorithms
L. Csanky · 1976
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Describing graphs: A first-order approach to graph canonization
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An optimal lower bound on the number of variables for graph identifications
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FAQ: Questions Asked Frequently
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Convolutional neural networks on graphs with fast localized spectral filtering
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Homomorphisms are a good basis for counting small subgraphs
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Neural message passing for quantum chemistry
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Bounded Variable Logics and Counting: A Study in Finite Models , volume 9 of Lecture Notes in Logic
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Weisfeiler and Leman go neural: Higher-order graph neural networks
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Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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The logical expressiveness of graph neural networks
Pablo Barceló, Egor V Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, and Juan Pablo Silva · 2020
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Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2020
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Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 2020
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A novel higher-order weisfeiler-lehman graph convolution
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Graph representation learning
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Graph neural networks with local graph parameters
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Weisfeiler and Lehman go topological: Message passing simplicial networks
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