Deconfounding to Explanation Evaluation in Graph Neural Networks

Deconfounding to Explanation Evaluation in Graph Neural Networks

21 January 2022

Xiang Wang

Papers citing "Deconfounding to Explanation Evaluation in Graph Neural Networks"

6 / 6 papers shown

Title
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook Wenzhao Jiang Hao Liu Hui Xiong CML AI4CE 28 2 0 19 Dec 2023
Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network Wendong Bi Bingbing Xu Xiaoqian Sun Li Xu Huawei Shen Xueqi Cheng 20 16 0 02 Feb 2023
Counterfactual Zero-Shot and Open-Set Visual Recognition Zhongqi Yue Tan Wang Hanwang Zhang Qianru Sun Xiansheng Hua BDL 153 192 0 01 Mar 2021
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 162 590 0 31 Dec 2020
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 186 913 0 02 Mar 2020
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