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Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-structured data.
Algorithm 97: shortest path
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An optimal lower bound on the number of variables for graph identification
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Resistance distance
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Algorithms for enumerating all spanning trees of undirected and weighted graphs
Sanjiv Kapoor and Hariharan Ramesh · 1995
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The electrical resistance of a graph captures its commute and cover times
Ashok K Chandra, Prabhakar Raghavan, Walter L Ruzzo, Roman Smolensky, and Prasoon Tiwari · 1996
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Finding and counting given length cycles
Noga Alon, Raphael Yuster, and Uri Zwick · 1997
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Comparing support vector machines with gaussian kernels to radial basis function classifiers
Bernhard Scholkopf, Kah-Kay Sung, Christopher JC Burges, Federico Girosi, Partha Niyogi, Tomaso Poggio, and Vladimir Vapnik · 1997
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Raphael Yuster and Uri Zwick · 1997
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Modern graph theory , volume 184
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Generalized inverse of the laplacian matrix and some applications
Ivan Gutman and W Xiao · 2004
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Mapping the us political blogosphere: Are conservative bloggers more prominent?
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Edwin R van Dam, Jack H Koolen, and Hajime Tanaka · 2014
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Graph isomorphism in quasipolynomial time
László Babai · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Xavier Bresson and Thomas Laurent · 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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Inductive representation learning on large graphs
William L Hamilton, Rex 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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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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struc2vec: Learning node representations from structural identity
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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On the equivalence between graph isomorphism testing and function approximation with gnns
Zhengdao Chen, Soledad Villar, Lei Chen, and Joan Bruna · 2019
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Universal invariant and equivariant graph neural networks
Nicolas Keriven and Gabriel Peyré · 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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Relational pooling for graph representations
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
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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Random features strengthen graph neural networks
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2021
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Neural trees for learning on graphs
Rajat Talak, Siyi Hu, Lisa Peng, and Luca Carlone · 2021
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Autobahn: Automorphism-based graph neural nets
Erik Thiede, Wenda Zhou, and Risi Kondor · 2021
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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
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From stars to subgraphs: Uplifting any gnn with local structure awareness
Lingxiao Zhao, Wei Jin, Leman Akoglu, and Neil Shah · 2019
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On weisfeiler-leman invariance: Subgraph counts and related graph properties
Vikraman Arvind, Frank Fuhlbrück, Johannes Köbler, and Oleg Verbitsky · 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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Jan Toenshoff, Martin Ritzert, Hinrikus Wolf, and Martin Grohe · 2021
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First place solution of kdd cup 2021 ogb large-scale challenge graph-level track
Chengxuan Ying, Mingqi Yang, Shuxin Zheng, Guolin Ke, Shengjie Luo, Tianle Cai, Chenglin Wu, Yuxin Wang, Yanming Shen, and Di He · 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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Shortest path networks for graph property prediction
Ralph Abboud, Radoslav Dimitrov, and Ismail Ilkan Ceylan · 2022
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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 · 2022
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Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos P Zafeiriou, and Michael Bronstein · 2022
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Weisfeiler and leman go infinite: Spectral and combinatorial pre-colorings
Or Feldman, Amit Boyarski, Shai Feldman, Dani Kogan, Avi Mendelson, and Chaim Baskin · 2022
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How powerful are k-hop message passing graph neural networks
Jiarui Feng, Yixin Chen, Fuhai Li, Anindya Sarkar, and Muhan Zhang · 2022
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Understanding and extending subgraph gnns by rethinking their symmetries
Fabrizio Frasca, Beatrice Bevilacqua, Michael Bronstein, and Haggai Maron · 2022
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Expressiveness and approximation properties of graph neural networks
Floris Geerts and Juan L Reutter · 2022
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Topological graph neural networks
Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, and Karsten Borgwardt · 2022
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Sign and basis invariant networks for spectral graph representation learning
Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, and Stefanie Jegelka · 2022
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Speqnets: Sparsity-aware permutation-equivariant graph networks
Christopher Morris, Gaurav Rattan, Sandra Kiefer, and Siamak Ravanbakhsh · 2022
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A theoretical comparison of graph neural network extensions
Pál András Papp and Roger Wattenhofer · 2022
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Ordered subgraph aggregation networks
Chendi Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, and Mathias Niepert · 2022
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The algebraic path problem for graph metrics
Enrique Fita Sanmartın, Sebastian Damrich, and Fred Hamprecht · 2022
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Benchmarking graphormer on large-scale molecular modeling datasets
Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, and Tie-Yan Liu · 2022
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Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein · 2022
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Message passing all the way up
Petar Veličković · 2022
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Ameya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Veličković, and Sreenivas Gollapudi · 2022
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A new perspective on” how graph neural networks go beyond weisfeiler-lehman?”
Asiri Wijesinghe and Qing Wang · 2022
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Boosting the cycle counting power of graph neural networks with i$^2$-GNNs
Yinan Huang, Xingang Peng, Jianzhu Ma, and Muhan Zhang · 2023
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