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Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
v1v2v3v4v5 (latest)

Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

4 October 2018
Christopher Morris
Martin Ritzert
Matthias Fey
William L. Hamilton
J. E. Lenssen
Gaurav Rattan
Martin Grohe
    GNN
ArXiv (abs)PDFHTML

Papers citing "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks"

50 / 877 papers shown
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node
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Giannis Nikolentzos
Michail Chatzianastasis
Michalis Vazirgiannis
188
9
0
04 Nov 2022
Towards Better Out-of-Distribution Generalization of Neural Algorithmic
  Reasoning Tasks
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks
Sadegh Mahdavi
Kevin Swersky
Thomas Kipf
Milad Hashemi
Christos Thrampoulidis
Renjie Liao
LRMOODNAI
248
32
0
01 Nov 2022
GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous
  Graphs
GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs
Marios Papachristou
Rishab Goel
Frank Portman
M. Miller
Rong Jin
324
0
0
01 Nov 2022
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in
  Materials Science
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
Santiago Miret
Kin Long Kelvin Lee
Carmelo Gonzales
Marcel Nassar
Matthew Spellings
193
19
0
31 Oct 2022
Improving Graph Neural Networks with Learnable Propagation Operators
Improving Graph Neural Networks with Learnable Propagation OperatorsInternational Conference on Machine Learning (ICML), 2022
Moshe Eliasof
Lars Ruthotto
Eran Treister
230
28
0
31 Oct 2022
PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
PAGE: Prototype-Based Model-Level Explanations for Graph Neural NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
Yong-Min Shin
Sun-Woo Kim
Won-Yong Shin
187
10
0
31 Oct 2022
A Comparative Study of Graph Neural Networks for Shape Classification in
  Neuroimaging
A Comparative Study of Graph Neural Networks for Shape Classification in NeuroimagingGEOmedia (GEOmedia), 2022
N. Shehata
Wulfie Bain
Ben Glocker
205
5
0
29 Oct 2022
Generalized Laplacian Positional Encoding for Graph Representation
  Learning
Generalized Laplacian Positional Encoding for Graph Representation Learning
Sohir Maskey
Alipanah Parviz
Maximilian Thiessen
Hannes Stärk
Ylli Sadikaj
Haggai Maron
AI4CE
293
23
0
28 Oct 2022
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Explaining the Explainers in Graph Neural Networks: a Comparative StudyACM Computing Surveys (ACM CSUR), 2022
Antonio Longa
Steve Azzolin
G. Santin
G. Cencetti
Pietro Lio
Bruno Lepri
Baptiste Caramiaux
343
46
0
27 Oct 2022
Federated Graph Representation Learning using Self-Supervision
Federated Graph Representation Learning using Self-Supervision
Susheel Suresh
Daniel Godbout
Arko Provo Mukherjee
Mayank Shrivastava
Jennifer Neville
Pan Li
OODFedML
156
2
0
27 Oct 2022
Meta-node: A Concise Approach to Effectively Learn Complex Relationships
  in Heterogeneous Graphs
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs
Jiwoong Park
Jisu Jeong
Kyungmin Kim
Hawook Jeong
142
1
0
26 Oct 2022
Boosting the Cycle Counting Power of Graph Neural Networks with
  I$^2$-GNNs
Boosting the Cycle Counting Power of Graph Neural Networks with I2^22-GNNsInternational Conference on Learning Representations (ICLR), 2022
Yinan Huang
Xingang Peng
Jianzhu Ma
Muhan Zhang
411
56
0
22 Oct 2022
Efficient Automatic Machine Learning via Design Graphs
Efficient Automatic Machine Learning via Design Graphs
Shirley Wu
Jiaxuan You
J. Leskovec
Rex Ying
GNN
209
1
0
21 Oct 2022
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
FoSR: First-order spectral rewiring for addressing oversquashing in GNNsInternational Conference on Learning Representations (ICLR), 2022
Kedar Karhadkar
P. Banerjee
Guido Montúfar
279
102
0
21 Oct 2022
Extending Graph Transformers with Quantum Computed Aggregation
Extending Graph Transformers with Quantum Computed Aggregation
Slimane Thabet
Romain Fouilland
L. Henriet
GNN
75
4
0
19 Oct 2022
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
Anti-Symmetric DGN: a stable architecture for Deep Graph NetworksInternational Conference on Learning Representations (ICLR), 2022
Alessio Gravina
D. Bacciu
Claudio Gallicchio
GNN
278
73
0
18 Oct 2022
A Practical, Progressively-Expressive GNN
A Practical, Progressively-Expressive GNNNeural Information Processing Systems (NeurIPS), 2022
Lingxiao Zhao
Louis Härtel
Neil Shah
Leman Akoglu
302
23
0
18 Oct 2022
Uplifting Message Passing Neural Network with Graph Original Information
Uplifting Message Passing Neural Network with Graph Original Information
Xiao Liu
Lijun Zhang
Hui Guan
GNN
182
4
0
08 Oct 2022
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of
  Graph Neural Networks for Attributed and Dynamic Graphs
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic GraphsNeural Networks (NN), 2022
Silvia Beddar-Wiesing
Giuseppe Alessio D’Inverno
C. Graziani
Veronica Lachi
Alice Moallemy-Oureh
F. Scarselli
J. M. Thomas
265
13
0
08 Oct 2022
ProGReST: Prototypical Graph Regression Soft Trees for Molecular
  Property Prediction
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property PredictionSDM (SDM), 2022
Dawid Rymarczyk
D. Dobrowolski
Tomasz Danel
259
6
0
07 Oct 2022
Expander Graph Propagation
Expander Graph PropagationLOG IN (LOG IN), 2022
Andreea Deac
Marc Lackenby
Petar Velivcković
356
74
0
06 Oct 2022
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph
  Neural Networks
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural NetworksNeural Information Processing Systems (NeurIPS), 2022
Ching-Yao Chuang
Stefanie Jegelka
OOD
211
43
0
04 Oct 2022
TPGNN: Learning High-order Information in Dynamic Graphs via Temporal
  Propagation
TPGNN: Learning High-order Information in Dynamic Graphs via Temporal Propagation
Zehong Wang
Qi Li
Donghua Yu
187
5
0
03 Oct 2022
Graph Neural Networks for Link Prediction with Subgraph Sketching
Graph Neural Networks for Link Prediction with Subgraph SketchingInternational Conference on Learning Representations (ICLR), 2022
B. Chamberlain
S. Shirobokov
Emanuele Rossi
Fabrizio Frasca
Thomas Markovich
Nils Y. Hammerla
Michael M. Bronstein
Max Hansmire
383
119
0
30 Sep 2022
Universal Prompt Tuning for Graph Neural Networks
Universal Prompt Tuning for Graph Neural NetworksNeural Information Processing Systems (NeurIPS), 2022
Taoran Fang
Yunchao Zhang
Yang Yang
Chunping Wang
Lei Chen
494
99
0
30 Sep 2022
Provably expressive temporal graph networks
Provably expressive temporal graph networksNeural Information Processing Systems (NeurIPS), 2022
Amauri Souza
Diego Mesquita
Samuel Kaski
Vikas Garg
262
71
0
29 Sep 2022
From Local to Global: Spectral-Inspired Graph Neural Networks
From Local to Global: Spectral-Inspired Graph Neural Networks
Ningyuan Huang
Soledad Villar
Carey E. Priebe
Da Zheng
Cheng-Fu Huang
Lin F. Yang
Vladimir Braverman
362
16
0
24 Sep 2022
A Generalist Neural Algorithmic Learner
A Generalist Neural Algorithmic LearnerLOG IN (LOG IN), 2022
Borja Ibarz
Vitaly Kurin
George Papamakarios
Kyriacos Nikiforou
Mehdi Abbana Bennani
...
Andreea Deac
Beatrice Bevilacqua
Yaroslav Ganin
Charles Blundell
Petar Velivcković
OOD
410
63
0
22 Sep 2022
Neural Graph Databases
Neural Graph DatabasesLOG IN (LOG IN), 2022
Maciej Besta
Patrick Iff
Florian Scheidl
Kazuki Osawa
Nikoli Dryden
Michal Podstawski
Tiancheng Chen
Torsten Hoefler
AI4CE
197
11
0
20 Sep 2022
Gradual Weisfeiler-Leman: Slow and Steady Wins the Race
Gradual Weisfeiler-Leman: Slow and Steady Wins the RaceLOG IN (LOG IN), 2022
Franka Bause
Nils M. Kriege
CLL
202
7
0
19 Sep 2022
Explainability in subgraphs-enhanced Graph Neural Networks
Explainability in subgraphs-enhanced Graph Neural Networks
Michele Guerra
Indro Spinelli
Simone Scardapane
F. Bianchi
121
1
0
16 Sep 2022
Towards Better Generalization with Flexible Representation of
  Multi-Module Graph Neural Networks
Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks
Hyungeun Lee
Kijung Yoon
AI4CE
214
2
0
14 Sep 2022
Defending Against Backdoor Attack on Graph Nerual Network by
  Explainability
Defending Against Backdoor Attack on Graph Nerual Network by Explainability
B. Jiang
Zhao Li
AAMLGNN
241
23
0
07 Sep 2022
Reinforced Continual Learning for Graphs
Reinforced Continual Learning for GraphsInternational Conference on Information and Knowledge Management (CIKM), 2022
Appan Rakaraddi
Siew-Kei Lam
Mahardhika Pratama
Marcus Vinícius de Carvalho
CLL
183
31
0
04 Sep 2022
Higher-order Clustering and Pooling for Graph Neural Networks
Higher-order Clustering and Pooling for Graph Neural NetworksInternational Conference on Information and Knowledge Management (CIKM), 2022
Alexandre Duval
Fragkiskos D. Malliaros
195
47
0
02 Sep 2022
TEP-GNN: Accurate Execution Time Prediction of Functional Tests using
  Graph Neural Networks
TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural NetworksInternational Conference on Product Focused Software Process Improvement (PROFES), 2022
H. Samoaa
Antonio Longa
Mazen Mohamad
M. Chehreghani
Philipp Leitner
GNN
184
14
0
25 Aug 2022
Self-Supervised Pretraining of Graph Neural Network for the Retrieval of
  Related Mathematical Expressions in Scientific Articles
Self-Supervised Pretraining of Graph Neural Network for the Retrieval of Related Mathematical Expressions in Scientific Articles
Lukas Pfahler
K. Morik
SSL
98
4
0
22 Aug 2022
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs
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Yijun Tian
Chuxu Zhang
Zhichun Guo
Xiangliang Zhang
Nitesh Chawla
356
17
0
22 Aug 2022
Graph Neural Network Based Node Deployment for Throughput Enhancement
Graph Neural Network Based Node Deployment for Throughput EnhancementIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
Yifei Yang
Dongmian Zou
Xiaofan He
227
9
0
19 Aug 2022
Robust Causal Graph Representation Learning against Confounding Effects
Robust Causal Graph Representation Learning against Confounding EffectsAAAI Conference on Artificial Intelligence (AAAI), 2022
Hang Gao
Jiangmeng Li
Jingyao Wang
Hui Xiong
Bing Xu
Changwen Zheng
Gang Hua
OODCML
116
20
0
18 Aug 2022
Generalizing Downsampling from Regular Data to Graphs
Generalizing Downsampling from Regular Data to GraphsAAAI Conference on Artificial Intelligence (AAAI), 2022
D. Bacciu
A. Conte
Francesco Landolfi
229
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0
06 Aug 2022
Graph neural networks for materials science and chemistry
Graph neural networks for materials science and chemistryCommunications Materials (Commun. Mater.), 2022
Patrick Reiser
Marlen Neubert
André Eberhard
Luca Torresi
Chen Zhou
...
Houssam Metni
Clint van Hoesel
Henrik Schopmans
T. Sommer
Pascal Friederich
GNNAI4CE
362
608
0
05 Aug 2022
See What the Robot Can't See: Learning Cooperative Perception for Visual
  Navigation
See What the Robot Can't See: Learning Cooperative Perception for Visual NavigationIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2022
J. Blumenkamp
Qingbiao Li
Binyu Wang
Yanfeng Guo
Amanda Prorok
SSL
259
3
0
01 Aug 2022
Physical Pooling Functions in Graph Neural Networks for Molecular
  Property Prediction
Physical Pooling Functions in Graph Neural Networks for Molecular Property PredictionComputers and Chemical Engineering (CCE), 2022
Artur M. Schweidtmann
Jan G. Rittig
Jana M. Weber
Martin Grohe
Manuel Dahmen
K. Leonhard
Alexander Mitsos
224
36
0
27 Jul 2022
Learning with Combinatorial Optimization Layers: a Probabilistic
  Approach
Learning with Combinatorial Optimization Layers: a Probabilistic Approach
Guillaume Dalle
Léo Baty
Louis Bouvier
Axel Parmentier
AI4CE
307
44
0
27 Jul 2022
Modelling Social Context for Fake News Detection: A Graph Neural Network
  Based Approach
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P. Saikia
Kshitij Gundale
A. Jain
Dev Jadeja
Harvi Patel
Mohendra Roy
GNN
93
13
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27 Jul 2022
Learning Hierarchical Protein Representations via Complete 3D Graph
  Networks
Learning Hierarchical Protein Representations via Complete 3D Graph NetworksInternational Conference on Learning Representations (ICLR), 2022
Limei Wang
Haoran Liu
Lu Dong
Jerry Kurtin
Shuiwang Ji
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184
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Graph neural networks for the prediction of molecular structure-property
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Artur M. Schweidtmann
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Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings
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David Hart
Michael Whitney
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