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Relational Pooling for Graph Representations

6 March 2019

R. Murphy

Balasubramaniam Srinivasan

Papers citing "Relational Pooling for Graph Representations"

50 / 167 papers shown

Title
Graph Neural Networks with Learnable Structural and Positional Representations Vijay Prakash Dwivedi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson GNN 292 330 0 15 Oct 2021
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness Lingxiao Zhao Wei Jin Leman Akoglu Neil Shah GNN 144 165 0 07 Oct 2021
Equivariant Subgraph Aggregation Networks Beatrice Bevilacqua Fabrizio Frasca Derek Lim Balasubramaniam Srinivasan Chen Cai G. Balamurugan M. Bronstein Haggai Maron 133 180 0 06 Oct 2021
Permute Me Softly: Learning Soft Permutations for Graph Representations Giannis Nikolentzos George Dasoulas Michalis Vazirgiannis GNN 77 9 0 05 Oct 2021
Reconstruction for Powerful Graph Representations Leonardo Cotta Christopher Morris Bruno Ribeiro AI4CE 196 79 0 01 Oct 2021
Graph Neural Networks for Graph Drawing Matteo Tiezzi Gabriele Ciravegna Marco Gori 87 21 0 21 Sep 2021
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Tianlong Chen Kaixiong Zhou Keyu Duan Wenqing Zheng Peihao Wang Helen Zhou Zhangyang Wang AAML GNN 65 66 0 24 Aug 2021
Global Self-Attention as a Replacement for Graph Convolution Md Shamim Hussain Mohammed J Zaki D. Subramanian ViT 101 127 0 07 Aug 2021
Generalization of graph network inferences in higher-order graphical models Yicheng Fei Xaq Pitkow 72 0 0 12 Jul 2021
Weisfeiler and Lehman Go Cellular: CW Networks Cristian Bodnar Fabrizio Frasca N. Otter Yu Guang Wang Pietro Lio Guido Montúfar M. Bronstein GNN 133 237 0 23 Jun 2021
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond Jonathan Godwin Michael Schaarschmidt Alex Gaunt Alvaro Sanchez-Gonzalez Yulia Rubanova Petar Velivcković J. Kirkpatrick Peter W. Battaglia 101 60 0 15 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 79 136 0 11 Jun 2021
Breaking the Limits of Message Passing Graph Neural Networks M. Balcilar Pierre Héroux Benoit Gaüzère Pascal Vasseur Sébastien Adam P. Honeine 88 128 0 08 Jun 2021
Neural Networks for Learning Counterfactual G-Invariances from Single Environments S Chandra Mouli Bruno Ribeiro OOD 77 12 0 20 Apr 2021
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity Mohammadamin Tavakoli Aaron Mood David Van Vranken Pierre Baldi GNN AI4CE 49 29 0 24 Mar 2021
On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions Jianfei Gao Bruno Ribeiro 82 11 0 12 Mar 2021
Size-Invariant Graph Representations for Graph Classification Extrapolations Beatrice Bevilacqua Yangze Zhou Bruno Ribeiro OOD 138 110 0 08 Mar 2021
Generalized Equivariance and Preferential Labeling for GNN Node Classification Zeyu Sun Wenjie Zhang Lili Mou Qihao Zhu Yingfei Xiong Lu Zhang 105 12 0 23 Feb 2021
Combinatorial optimization and reasoning with graph neural networks Quentin Cappart Didier Chételat Elias Boutros Khalil Andrea Lodi Christopher Morris Petar Velickovic AI4CE 115 361 0 18 Feb 2021
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing Jan Toenshoff Martin Ritzert Hinrikus Wolf Martin Grohe GNN 166 33 0 17 Feb 2021
Learning Graph Representations Rucha Bhalchandra Joshi Subhankar Mishra GNN AI4CE 12 5 0 03 Feb 2021
Identity-aware Graph Neural Networks Jiaxuan You Jonathan M. Gomes-Selman Rex Ying J. Leskovec 62 259 0 25 Jan 2021
A pipeline for fair comparison of graph neural networks in node classification tasks Wentao Zhao Dalin Zhou X. Qiu Wei Jiang 59 5 0 19 Dec 2020
A Generalization of Transformer Networks to Graphs Vijay Prakash Dwivedi Xavier Bresson AI4CE 115 763 0 17 Dec 2020
Graph Neural Networks: Taxonomy, Advances and Trends Yu Zhou Haixia Zheng Xin Huang Shufeng Hao Dengao Li Jumin Zhao AI4TS 166 125 0 16 Dec 2020
Breaking the Expressive Bottlenecks of Graph Neural Networks Mingqi Yang Yanming Shen Heng Qi Baocai Yin 72 10 0 14 Dec 2020
Adversarial Permutation Guided Node Representations for Link Prediction Indradyumna Roy A. De Soumen Chakrabarti 66 15 0 13 Dec 2020
Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results B. Tahmasebi Derek Lim Stefanie Jegelka GNN 137 30 0 06 Dec 2020
Graph convolutions that can finally model local structure Rémy Brossard Oriel Frigo David Dehaene GNN 114 48 0 30 Nov 2020
Design Space for Graph Neural Networks Jiaxuan You Rex Ying J. Leskovec GNN AI4CE 204 323 0 17 Nov 2020
Handling Missing Data with Graph Representation Learning Jiaxuan You Xiaobai Ma D. Ding Mykel Kochenderfer J. Leskovec 82 182 0 30 Oct 2020
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks Hao Tang Zhiao Huang Jiayuan Gu Bao-Liang Lu Hao Su AI4CE 70 9 0 26 Oct 2020
Graph Information Bottleneck Tailin Wu Hongyu Ren Pan Li J. Leskovec AAML 165 240 0 24 Oct 2020
Rethinking pooling in graph neural networks Diego Mesquita Amauri Souza Samuel Kaski GNN AI4CE 293 118 0 22 Oct 2020
Unsupervised Joint $k$ -node Graph Representations with Compositional Energy-Based Models Leonardo Cotta Carlos H. C. Teixeira A. Swami Bruno Ribeiro SSL 72 9 0 08 Oct 2020
GraphITE: Estimating Individual Effects of Graph-structured Treatments Shonosuke Harada H. Kashima CML 97 23 0 29 Sep 2020
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing Ziwei Zhang Chenhao Niu Peng Cui Jian Pei Bo Zhang Wenwu Zhu 61 2 0 05 Sep 2020
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning Pan Li Yanbang Wang Hongwei Wang J. Leskovec GNN 101 12 0 31 Aug 2020
Expressive Power of Invariant and Equivariant Graph Neural Networks Waïss Azizian Marc Lelarge 106 111 0 28 Jun 2020
Building powerful and equivariant graph neural networks with structural message-passing Clément Vignac Andreas Loukas P. Frossard 98 123 0 26 Jun 2020
Hierarchical Inter-Message Passing for Learning on Molecular Graphs Matthias Fey Jan-Gin Yuen F. Weichert GNN 100 86 0 22 Jun 2020
Subgraph Neural Networks Emily Alsentzer S. G. Finlayson Michelle M. Li Marinka Zitnik GNN 150 139 0 18 Jun 2020
Quantifying Challenges in the Application of Graph Representation Learning Antonia Gogoglou C. Bayan Bruss Brian Nguyen Reza Sarshogh Keegan E. Hines 20 2 0 18 Jun 2020
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting Giorgos Bouritsas Fabrizio Frasca Stefanos Zafeiriou M. Bronstein 136 439 0 16 Jun 2020
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs Ziwei Zhang Peng Cui J. Pei Xin Eric Wang Wenwu Zhu GNN 84 33 0 08 Jun 2020
The Impact of Global Structural Information in Graph Neural Networks Applications Davide Buffelli Fabio Vandin AI4CE 66 8 0 06 Jun 2020
How hard is to distinguish graphs with graph neural networks? Andreas Loukas GNN 84 6 0 13 May 2020
Deep Constraint-based Propagation in Graph Neural Networks Matteo Tiezzi G. Marra S. Melacci Marco Maggini AI4CE GNN 73 13 0 05 May 2020
Graph Homomorphism Convolution Hoang NT Takanori Maehara 170 41 0 03 May 2020
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning Yanyan Liang Yanfeng Zhang Dechao Gao Qian Xu GNN 22 5 0 15 Apr 2020