What graph neural networks cannot learn: depth vs width

6 July 2019

Papers citing "What graph neural networks cannot learn: depth vs width"

50 / 66 papers shown

Title
Towards Invariance to Node Identifiers in Graph Neural Networks Maya Bechler-Speicher Moshe Eliasof Carola-Bibiane Schönlieb Ran Gilad-Bachrach Amir Globerson 65 1 0 20 Feb 2025
Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction Giuseppe Alessio DÍnverno Saeid Moradizadeh Sajad Salavatidezfouli Pasquale Claudio Africa G. Rozza AI4CE 38 0 0 04 Oct 2024
Revisiting Random Walks for Learning on Graphs Jinwoo Kim Olga Zaghen Ayhan Suleymanzade Youngmin Ryou Seunghoon Hong 59 0 0 01 Jul 2024
Lightweight Spatial Modeling for Combinatorial Information Extraction From Documents Yanfei Dong Lambert Deng Jiazheng Zhang Xiaodong Yu Ting Lin Francesco Gelli Soujanya Poria W. Lee 40 0 0 08 May 2024
A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms Raffaele Marino L. Buffoni Bogdan Zavalnij GNN 40 5 0 13 Mar 2024
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning Hanxuan Yang Qingchao Kong Wenji Mao BDL 20 0 0 09 Dec 2023
Going beyond persistent homology using persistent homology Johanna Immonen Amauri H. Souza Vikas K. Garg 38 9 0 10 Nov 2023
The Expressive Power of Graph Neural Networks: A Survey Bingxue Zhang Changjun Fan Shixuan Liu Kuihua Huang Xiang Zhao Jin-Yu Huang Zhong Liu 40 19 0 16 Aug 2023
Weisfeiler and Leman Go Measurement Modeling: Probing the Validity of the WL Test Arjun Subramonian Adina Williams Maximilian Nickel Yizhou Sun Levent Sagun 28 0 0 11 Jul 2023
Graph Inductive Biases in Transformers without Message Passing Liheng Ma Chen Lin Derek Lim Adriana Romero Soriano P. Dokania Mark J. Coates Philip H. S. Torr Ser-Nam Lim AI4CE 31 85 0 27 May 2023
Descriptive complexity for distributed computing with circuits Veeti Ahvonen Damian Heiman L. Hella Antti Kuusisto 24 4 0 08 Mar 2023
Equivariant Polynomials for Graph Neural Networks Omri Puny Derek Lim B. Kiani Haggai Maron Y. Lipman 28 31 0 22 Feb 2023
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure Ryoma Sato 34 5 0 26 Jan 2023
Everything is Connected: Graph Neural Networks Petar Velickovic GNN AI4CE 22 179 0 19 Jan 2023
A Generalization of ViT/MLP-Mixer to Graphs Xiaoxin He Bryan Hooi T. Laurent Adam Perold Yann LeCun Xavier Bresson 47 88 0 27 Dec 2022
On the Ability of Graph Neural Networks to Model Interactions Between Vertices Noam Razin Tom Verbin Nadav Cohen 23 10 0 29 Nov 2022
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks Anders Aamand Justin Y. Chen Piotr Indyk Shyam Narayanan R. Rubinfeld Nicholas Schiefer Sandeep Silwal Tal Wagner 39 21 0 06 Nov 2022
Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Network Yi Yi Xu Wan Kangfei Zhao Ou-Yang Le Pei-Ying Zhao 21 1 0 27 Oct 2022
Boosting the Cycle Counting Power of Graph Neural Networks with I $^2$ -GNNs Yinan Huang Xingang Peng Jianzhu Ma Muhan Zhang 84 47 0 22 Oct 2022
Low-Rank Representations Towards Classification Problem of Complex Networks Murat Çelik Ali Baran Tasdemir Lale Özkahya GNN 11 0 0 20 Oct 2022
On Classification Thresholds for Graph Attention with Edge Features K. Fountoulakis Dake He Silvio Lattanzi Bryan Perozzi Anton Tsitsulin Shenghao Yang GNN 30 6 0 18 Oct 2022
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs Silvia Beddar-Wiesing Giuseppe Alessio D’Inverno C. Graziani Veronica Lachi Alice Moallemy-Oureh F. Scarselli J. M. Thomas 31 9 0 08 Oct 2022
Provably expressive temporal graph networks Amauri Souza Diego Mesquita Samuel Kaski Vikas K. Garg 89 54 0 29 Sep 2022
On Representing Linear Programs by Graph Neural Networks Ziang Chen Jialin Liu Xinshang Wang Jian Lu W. Yin AI4CE 60 31 0 25 Sep 2022
On the Privacy Risks of Cell-Based NAS Architectures Haiping Huang Zhikun Zhang Yun Shen Michael Backes Qi Li Yang Zhang 27 7 0 04 Sep 2022
Agent-based Graph Neural Networks Karolis Martinkus Pál András Papp Benedikt Schesch Roger Wattenhofer LLMAG GNN 29 17 0 22 Jun 2022
Universally Expressive Communication in Multi-Agent Reinforcement Learning Matthew Morris Thomas D. Barrett Arnu Pretorius 24 4 0 14 Jun 2022
Shortest Path Networks for Graph Property Prediction Ralph Abboud Radoslav Dimitrov .Ismail .Ilkan Ceylan GNN 27 45 0 02 Jun 2022
Recipe for a General, Powerful, Scalable Graph Transformer Ladislav Rampášek Mikhail Galkin Vijay Prakash Dwivedi A. Luu Guy Wolf Dominique Beaini 57 515 0 25 May 2022
Not too little, not too much: a theoretical analysis of graph (over)smoothing Nicolas Keriven 41 88 0 24 May 2022
Theory of Graph Neural Networks: Representation and Learning Stefanie Jegelka GNN AI4CE 33 68 0 16 Apr 2022
Graph Neural Networks for Wireless Communications: From Theory to Practice Yifei Shen Jun Zhang Shenghui Song Khaled B. Letaief GNN AI4CE 25 110 0 21 Mar 2022
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets Yu Shi Shuxin Zheng Guolin Ke Yifei Shen Jiacheng You Jiyan He Shengjie Luo Chang-Shu Liu Di He Tie-Yan Liu AI4CE 42 65 0 09 Mar 2022
A Theoretical Comparison of Graph Neural Network Extensions Pál András Papp Roger Wattenhofer 100 46 0 30 Jan 2022
A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants Ningyuan Huang Soledad Villar 24 62 0 18 Jan 2022
Equivariant Quantum Graph Circuits Péter Mernyei K. Meichanetzidis .Ismail .Ilkan Ceylan 36 8 0 10 Dec 2021
OOD-GNN: Out-of-Distribution Generalized Graph Neural Network Haoyang Li Xin Wang Ziwei Zhang Wenwu Zhu OODD OOD 26 97 0 07 Dec 2021
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition H. Sahbi GNN 23 27 0 06 Dec 2021
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks Pál András Papp Karolis Martinkus Lukas Faber Roger Wattenhofer GNN 22 138 0 11 Nov 2021
On Provable Benefits of Depth in Training Graph Convolutional Networks Weilin Cong M. Ramezani M. Mahdavi 24 73 0 28 Oct 2021
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization Mucong Ding Kezhi Kong Jingling Li Chen Zhu John P. Dickerson Furong Huang Tom Goldstein GNN MQ 33 47 0 27 Oct 2021
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness Lingxiao Zhao Wei Jin L. Akoglu Neil Shah GNN 24 160 0 07 Oct 2021
Equivariant Subgraph Aggregation Networks Beatrice Bevilacqua Fabrizio Frasca Derek Lim Balasubramaniam Srinivasan Chen Cai G. Balamurugan M. Bronstein Haggai Maron 53 175 0 06 Oct 2021
Graph Neural Networks: Methods, Applications, and Opportunities Lilapati Waikhom Ripon Patgiri GNN 34 42 0 24 Aug 2021
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks Zhiqian Chen Fanglan Chen Lei Zhang Taoran Ji Kaiqun Fu Liang Zhao Feng Chen Lingfei Wu Charu C. Aggarwal Chang-Tien Lu 38 18 0 21 Jul 2021
Graph Neural Networks with Local Graph Parameters Pablo Barceló Floris Geerts Juan L. Reutter Maksimilian Ryschkov 24 65 0 12 Jun 2021
Scalars are universal: Equivariant machine learning, structured like classical physics Soledad Villar D. Hogg Kate Storey-Fisher Weichi Yao Ben Blum-Smith PINN AI4CE 24 130 0 11 Jun 2021
Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks H. Sahbi 3DH GNN 18 3 0 09 Apr 2021
Combinatorial optimization and reasoning with graph neural networks Quentin Cappart Didier Chételat Elias Boutros Khalil Andrea Lodi Christopher Morris Petar Velickovic AI4CE 32 347 0 18 Feb 2021
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization Aseem Baranwal K. Fountoulakis Aukosh Jagannath OODD 39 75 0 13 Feb 2021