DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks

11 November 2021

Roger Wattenhofer

Papers citing "DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks"

50 / 98 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 51 1 0 20 Feb 2025
Effects of Random Edge-Dropping on Over-Squashing in Graph Neural Networks Jasraj Singh Keyue Jiang Brooks Paige Laura Toni 65 1 0 11 Feb 2025
Using Random Noise Equivariantly to Boost Graph Neural Networks Universally X. Wang Muhan Zhang 102 0 0 04 Feb 2025
Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Joshua Southern Yam Eitan Guy Bar-Shalom Michael M. Bronstein Haggai Maron Fabrizio Frasca 28 0 0 06 Jan 2025
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements Haoyang Li Y. Xu C. Zhang Alexander Zhou Lei Chen Qing Li AI4CE 76 0 0 03 Jan 2025
Efficient Link Prediction via GNN Layers Induced by Negative Sampling Yuxin Wang Xiannian Hu Quan Gan Xuanjing Huang Xipeng Qiu David Wipf 56 4 0 31 Dec 2024
On the Utilization of Unique Node Identifiers in Graph Neural Networks Maya Bechler-Speicher Moshe Eliasof Carola-Bibiane Schönlieb Ran Gilad-Bachrach Amir Globerson 28 0 0 04 Nov 2024
Sparse Covariance Neural Networks Andrea Cavallo Zhan Gao Elvin Isufi 21 0 0 02 Oct 2024
Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning Qincheng Lu Jiaqi Zhu Sitao Luan Xiao-Wen Chang 28 2 0 15 Sep 2024
Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets Shivesh Prakash 33 0 0 07 Sep 2024
Graph Classification via Reference Distribution Learning: Theory and Practice Zixiao Wang Jicong Fan 18 0 0 21 Aug 2024
xAI-Drop: Don't Use What You Cannot Explain Vincenzo Marco De Luca Antonio Longa Andrea Passerini Pietro Lio' 29 0 0 29 Jul 2024
Foundations and Frontiers of Graph Learning Theory Yu Huang Min Zhou Menglin Yang Zhen Wang Muhan Zhang Jie Wang Hong Xie Hao Wang Defu Lian Enhong Chen AI4CE GNN 43 2 0 03 Jul 2024
Revisiting Random Walks for Learning on Graphs Jinwoo Kim Olga Zaghen Ayhan Suleymanzade Youngmin Ryou Seunghoon Hong 54 0 0 01 Jul 2024
Synergistic Deep Graph Clustering Network Benyu Wu Shifei Ding Xiao Xu Lili Guo Ling Ding Xindong Wu 28 0 0 22 Jun 2024
Demystifying Higher-Order Graph Neural Networks Maciej Besta Florian Scheidl Lukas Gianinazzi S. Klaiman Jürgen Müller Torsten Hoefler 38 2 0 18 Jun 2024
Scalable Expressiveness through Preprocessed Graph Perturbations Danial Saber Amirali Salehi-Abari 22 1 0 17 Jun 2024
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening Guy Bar-Shalom Yam Eitan Fabrizio Frasca Haggai Maron 21 1 0 13 Jun 2024
GATE: How to Keep Out Intrusive Neighbors Nimrah Mustafa R. Burkholz 28 0 0 01 Jun 2024
FlexiDrop: Theoretical Insights and Practical Advances in Random Dropout Method on GNNs Zhiheng Zhou Sihao Liu Weichen Zhao 22 0 0 30 May 2024
Probabilistic Graph Rewiring via Virtual Nodes Chendi Qian Andrei Manolache Christopher Morris Mathias Niepert AI4CE 28 3 0 27 May 2024
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models Zhenzhong Wang Zehui Lin Wanyu Lin Ming Yang Minggang Zeng Kay Chen Tan 16 3 0 25 May 2024
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks Rongrong Ma Guansong Pang Ling-Hao Chen AI4CE 33 0 0 17 May 2024
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More Fanchen Bu Hyeonsoo Jo Soo Yong Lee Sungsoo Ahn Kijung Shin 22 3 0 14 May 2024
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships Qi Zou Na Yu Daoliang Zhang Wei Zhang Rui Gao AI4CE 27 1 0 07 May 2024
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers Wenhao Zhu Guojie Song Liangji Wang Shaoguo Liu 35 0 0 06 May 2024
CORE: Data Augmentation for Link Prediction via Information Bottleneck Kaiwen Dong Zhichun Guo Nitesh V. Chawla 17 0 0 17 Apr 2024
Temporal Generalization Estimation in Evolving Graphs Bin Lu Tingyan Ma Xiaoying Gan Xinbing Wang Yunqiang Zhu Cheng Zhou Shiyu Liang 24 1 0 07 Apr 2024
Thermal Earth Model for the Conterminous United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN) M. Aljubran Roland N. Horne AI4CE 31 1 0 15 Mar 2024
Representation Learning on Heterophilic Graph with Directional Neighborhood Attention Qincheng Lu Jiaqi Zhu Sitao Luan Xiaomeng Chang 26 6 0 03 Mar 2024
Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking Simon Zhang Cheng Xin Tamal K. Dey 35 1 0 17 Feb 2024
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products Guy Bar-Shalom Beatrice Bevilacqua Haggai Maron AI4CE 20 6 0 13 Feb 2024
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings Nurudin Alvarez-Gonzalez Andreas Kaltenbrunner Vicencc Gómez 12 2 0 10 Dec 2023
Efficient Subgraph GNNs by Learning Effective Selection Policies Beatrice Bevilacqua Moshe Eliasof E. Meirom Bruno Ribeiro Haggai Maron 18 13 0 30 Oct 2023
Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes Cai Zhou Xiyuan Wang Muhan Zhang 31 14 0 30 Oct 2023
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network Lecheng Kong Jiarui Feng Hao Liu Dacheng Tao Yixin Chen Muhan Zhang AI4CE 8 11 0 29 Oct 2023
Artifact-Robust Graph-Based Learning in Digital Pathology Saba Heidari Gheshlaghi Milan Aryal Nasim Yahyasoltani Masoud Ganji OOD 17 0 0 27 Oct 2023
Balancing Augmentation with Edge-Utility Filter for Signed GNNs Ke-Jia Chen Yaming Ji Youran Qu Chuhan Xu 8 0 0 25 Oct 2023
Are GATs Out of Balance? Nimrah Mustafa Aleksandar Bojchevski R. Burkholz 41 4 0 11 Oct 2023
Exponential Quantum Communication Advantage in Distributed Inference and Learning H. Michaeli D. Gilboa Daniel Soudry Jarrod R. McClean FedML 11 0 0 11 Oct 2023
Flood and Echo Net: Algorithmically Aligned GNNs that Generalize Joël Mathys Florian Grötschla K. Nadimpalli Roger Wattenhofer FedML 44 0 0 10 Oct 2023
Supercharging Graph Transformers with Advective Diffusion Qitian Wu Chenxiao Yang Kaipeng Zeng Fan Nie AI4CE 37 6 0 10 Oct 2023
Probabilistically Rewired Message-Passing Neural Networks Chendi Qian Andrei Manolache Kareem Ahmed Zhe Zeng Guy Van den Broeck Mathias Niepert Christopher Morris 26 11 0 03 Oct 2023
Cooperative Graph Neural Networks Ben Finkelshtein Xingyue Huang Michael M. Bronstein .Ismail .Ilkan Ceylan GNN 22 19 0 02 Oct 2023
Pure Message Passing Can Estimate Common Neighbor for Link Prediction Kaiwen Dong Zhichun Guo Nitesh V. Chawla 19 7 0 02 Sep 2023
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability Zehao Dong Muhan Zhang Philip R. O. Payne Michael Province C. Cruchaga Tianyu Zhao Fuhai Li Yixin Chen 30 1 0 01 Sep 2023
Graph Out-of-Distribution Generalization with Controllable Data Augmentation Bin Lu Xiaoying Gan Ze Zhao Shiyu Liang Luoyi Fu Xinbing Wang Cheng Zhou 19 6 0 16 Aug 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 Lehman Go Paths: Learning Topological Features via Path Complexes Quang Truong Peter Chin GNN 13 7 0 13 Aug 2023
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling Chuang Liu Yibing Zhan Baosheng Yu Liu Liu Bo Du Wenbin Hu Tongliang Liu 14 11 0 22 Jun 2023