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While (message-passing) graph neural networks have clear limitations in approximating permutation-equivariant functions over graphs or general relational data, more expressive, higher-order graph neural networks do not scale to large graphs.
Provably powerful graph networks
H. Maron, H. Ben-Hamu, H. Serviansky, and Y. Lipman · 1905
Earlier work this paper cites.
Alchemy: A quantum chemistry dataset for benchmarking AI models
G. Chen, P. Chen, C. Hsieh, C. Lee, B. Liao, R. Liao, W. Liu, J. Qiu, Q. Sun, J. Tang, R. S. Zemel, and S. Zhang · 1906
Earlier work this paper cites.
Finite Graphs and Networks: An Introduction with Applications
R. G. Busacker and T. L. Saaty · 1965
Earlier work this paper cites.
The reduction of a graph to canonical form and the algebra which appears therein
B. Weisfeiler and A. Leman · 1968
Earlier work this paper cites.
On Construction and Identification of Graphs
B. Weisfeiler · 1976
Earlier work this paper cites.
Lectures on graph isomorphism
L. Babai · 1979
Earlier work this paper cites.
Describing graphs: A first-order approach to graph canonization
N. Immerman and E. Lander · 1990
Earlier work this paper cites.
An optimal lower bound on the number of variables for graph identifications
J. Cai, M. Fürer, and N. Immerman · 1992
Earlier work this paper cites.
Chemnet: A novel neural network based method for graph/property mapping
D. B. Kireev · 1995
Earlier work this paper cites.
A neural device for searching direct correlations between structures and properties of chemical compounds
I. I. Baskin, V. A. Palyulin, and N. S. Zefirov · 1997
Earlier work this paper cites.
Supervised neural networks for the classification of structures
A. Sperduti and A. Starita · 1997
Earlier work this paper cites.
Algorithms on Trees and Graphs
G. Valiente · 2002
Earlier work this paper cites.
On graph kernels: Hardness results and efficient alternatives
T. Gärtner, P. Flach, and S. Wrobel · 2003
Earlier work this paper cites.
Marginalized kernels between labeled graphs
H. Kashima, K. Tsuda, and A. Inokuchi · 2003
Earlier work this paper cites.
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A.-L. Barabasi and Z. N. Oltvai · 2004
Earlier work this paper cites.
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K. M. Borgwardt and H.-P. Kriegel · 2005
Earlier work this paper cites.
Automatic generation of complementary descriptors with molecular graph networks
C. Merkwirth and T. Lengauer · 2005
Earlier work this paper cites.
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A. Micheli and A. S. Sestito · 2005
Earlier work this paper cites.
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C. Morris, N. M. Kriege, F. Bause, K. Kersting, P. Mutzel, and M. Neumann · 2007
Earlier work this paper cites.
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A. Micheli · 2009
Earlier work this paper cites.
The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
Earlier work this paper cites.
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N. Shervashidze, S. V. N. Vishwanathan, T. H. Petri, K. Mehlhorn, and K. M. Borgwardt · 2009
Earlier work this paper cites.
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D. Easley and J. Kleinberg · 2010
Earlier work this paper cites.
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C.-C. Chang and C.-J. Lin · 2011
Earlier work this paper cites.
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N. Shervashidze, P. Schweitzer, E. J. van Leeuwen, K. Mehlhorn, and K. M. Borgwardt · 2011
Earlier work this paper cites.
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A. Atserias and E. N. Maneva · 2013
Earlier work this paper cites.
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J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
Earlier work this paper cites.
Sherali–adams relaxations of graph isomorphism polytopes
P. N. Malkin · 2014
Earlier work this paper cites.
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R. Ramakrishnan, O. Dral, P., M. Rupp, and O. A. von Lilienfeld · 2014
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V. Arvind, J. Köbler, G. Rattan, and O. Verbitsky · 2015
Earlier work this paper cites.
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D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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M. Grohe and M. Otto · 2015
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Earlier work this paper cites.
Graphs identified by logics with counting
S. Kiefer, P. Schweitzer, and E. Selman · 2015
Earlier work this paper cites.
Graph isomorphism in quasipolynomial time
L. Babai · 2016
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M. Defferrard, B. X., and P. Vandergheynst · 2016
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S. Kiefer and P. Schweitzer · 2016
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R. Kondor and H. Pan · 2016
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N. M. Kriege, P.-L. Giscard, and R. C. Wilson · 2016
Earlier work this paper cites.
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