Exploring Causal Learning through Graph Neural Networks: An In-depth
Review

Exploring Causal Learning through Graph Neural Networks: An In-depth Review

25 November 2023

Haoran Xie

Papers citing "Exploring Causal Learning through Graph Neural Networks: An In-depth Review"

9 / 9 papers shown

Title
Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda Nanjangud C. Narendra Nithin Nagaraj OOD CML 36 0 0 23 Jan 2025
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook Wenzhao Jiang Hao Liu Hui Xiong CML AI4CE 33 2 0 19 Dec 2023
Investigating Transfer Learning in Graph Neural Networks Nishai Kooverjee Steven D. James Terence L van Zyl GNN 31 14 0 01 Feb 2022
Relating Graph Neural Networks to Structural Causal Models Matej Zečević D. Dhami Petar Velickovic Kristian Kersting AI4CE CML 55 53 0 09 Sep 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 110 142 0 05 Feb 2021
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 164 590 0 31 Dec 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 278 11,677 0 09 Mar 2017
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 162 1,775 0 02 Mar 2017
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 234 1,811 0 25 Nov 2016