Size-Invariant Graph Representations for Graph Classification
Extrapolations

Size-Invariant Graph Representations for Graph Classification Extrapolations

8 March 2021

Beatrice Bevilacqua

Papers citing "Size-Invariant Graph Representations for Graph Classification Extrapolations"

12 / 12 papers shown

Title
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation Kexin Zhang Shuhan Liu Song Wang Weili Shi Chen Chen Pan Li Sheng R. Li Jundong Li Kaize Ding OOD 40 2 0 25 Oct 2024
The CLRS Algorithmic Reasoning Benchmark Petar Velivcković Adria Puigdomenech Badia David Budden Razvan Pascanu Andrea Banino Mikhail Dashevskiy R. Hadsell Charles Blundell 151 86 0 31 May 2022
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs Yangze Zhou Gitta Kutyniok Bruno Ribeiro OODD AI4CE 64 37 0 30 May 2022
Reconstruction for Powerful Graph Representations Leonardo Cotta Christopher Morris Bruno Ribeiro AI4CE 117 78 0 01 Oct 2021
Combinatorial optimization and reasoning with graph neural networks Quentin Cappart Didier Chételat Elias Boutros Khalil Andrea Lodi Christopher Morris Petar Velickovic AI4CE 28 344 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 68 21 0 17 Feb 2021
From Local Structures to Size Generalization in Graph Neural Networks Gilad Yehudai Ethan Fetaya E. Meirom Gal Chechik Haggai Maron GNN AI4CE 125 112 0 17 Oct 2020
A Survey on The Expressive Power of Graph Neural Networks Ryoma Sato 159 158 0 09 Mar 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx) David M. Krueger Ethan Caballero J. Jacobsen Amy Zhang Jonathan Binas Dinghuai Zhang Rémi Le Priol Aaron Courville OOD 212 888 0 02 Mar 2020
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 217 1,726 0 09 Jun 2018
Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia Razvan Pascanu Matthew Lai Danilo Jimenez Rezende Koray Kavukcuoglu AI4CE OCL PINN GNN 252 1,394 0 01 Dec 2016
Learning Representations for Counterfactual Inference Fredrik D. Johansson Uri Shalit David Sontag CML OOD BDL 205 713 0 12 May 2016