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We study and compare different Graph Neural Network extensions that increase the expressive power of GNNs beyond the Weisfeiler-Leman test.
An optimal lower bound on the number of variables for graph identification
Jin-Yi Cai, Martin Fürer, and Neil Immerman · 1992
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Community structure in social and biological networks
M. Girvan and M. E. J. Newman · 2002
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Network motifs: Simple building blocks of complex networks
R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon · 2002
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Protein interface prediction using graph convolutional networks
Alex Fout, Jonathon Byrd, Basir Shariat, and Asa Ben-Hur · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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The Power of the Weisfeiler-Leman Algorithm to Decompose Graphs
Sandra Kiefer and Daniel Neuen · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
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Weisfeiler and Leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Approximation ratios of graph neural networks for combinatorial problems
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
On weisfeiler-leman invariance: Subgraph counts and related graph properties
V. Arvind, Frank Fuhlbrück, Johannes Köbler, and Oleg Verbitsky · 2020
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Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M Bronstein · 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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Generalization and representational limits of graph neural networks
Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
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How hard is to distinguish graphs with graph neural networks?
Graph neural networks with local graph parameters
Pablo Barceló, Floris Geerts, Juan Reutter, and Maksimilian Ryschkov · 2021
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Equivariant subgraph aggregation networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M Bronstein, and Haggai Maron · 2021
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Weisfeiler and lehman go cellular: CW networks
Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, and Michael M. Bronstein · 2021
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Weisfeiler and lehman go topological: Message passing simplicial networks
Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F Montufar, Pietro Lió, and Michael Bronstein · 2021
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Reconstruction for powerful graph representations
Leonardo Cotta, Christopher Morris, and Bruno Ribeiro · 2021
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Andreas Loukas · 2020
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What graph neural networks cannot learn: depth vs width
Andreas Loukas · 2020
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia · 2020
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A survey on the expressive power of graph neural networks
Ryoma Sato · 2020
Cited alongside, same era.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Cited alongside, same era.
The surprising power of graph neural networks with random node initialization
Ralph Abboud, Ismail Ilkan Ceylan, Martin Grohe, and Thomas Lukasiewicz · 2021
Cited alongside, same era.
Weisfeiler and leman go machine learning: The story so far
Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M Kriege, Martin Grohe, Matthias Fey, and Karsten Borgwardt · 2021
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DropGNN: Random dropouts increase the expressiveness of graph neural networks
Pál András Papp, Karolis Martinkus, Lukas Faber, and Roger Wattenhofer · 2021
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Ego-GNNs: Exploiting ego structures in graph neural networks
Dylan Sandfelder, Priyesh Vijayan, and William L. Hamilton · 2021
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Random features strengthen graph neural networks
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2021
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Identity-aware graph neural networks
Jiaxuan You, Jonathan M Gomes-Selman, Rex Ying, and Jure Leskovec · 2021
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Nested graph neural networks
Muhan Zhang and Pan Li · 2021
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