Fetching the paper…
Reading the bibliography…
Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predictions.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Torch: A scientific computing framework for luajit
Ronan Collobert, Koray Kavukcuoglu, and Clément Farabet · 2011
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Earlier work this paper cites.
A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2018
Earlier work this paper cites.
Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Earlier work this paper cites.
Graph attention networks, 2018
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Earlier work this paper cites.
Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Earlier work this paper cites.
Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour · 2019
Earlier work this paper cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Earlier work this paper cites.
Explainability methods for graph convolutional neural networks
Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, and Heiko Hoffmann · 2019
Earlier work this paper cites.
Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
Cited alongside, same era.
Contrastive graph neural network explanation
Lukas Faber, Amin K Moghaddam, and Roger Wattenhofer · 2020
Cited alongside, same era.
Strategies for pre-training graph neural networks, 2020
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
Cited alongside, same era.
Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
Cited alongside, same era.
Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
Cited alongside, same era.
Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
On explainability of graph neural networks via subgraph explorations
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji · 2021
Later among the works it cites.
Evaluating explainability for graph neural networks
Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, and Marinka Zitnik · 2022
Later among the works it cites.
A comparative study of faithfulness metrics for model interpretability methods
Chun Sik Chan, Huanqi Kong, and Liang Guanqing · 2022
Later among the works it cites.
Graph neural networks: Adversarial robustness
Stephan Günnemann · 2022
Later among the works it cites.
Explainability in graph neural networks: An experimental survey
Peibo Li, Yixing Yang, Maurice Pagnucco, and Yang Song · 2022
Later among the works it cites.
Explaining the explainers in graph neural networks: a comparative study
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Minh N. Vu and My T. Thai · 2020
Cited alongside, same era.
Probing GNN explainers: A rigorous theoretical and empirical analysis of GNN explanation methods
Chirag Agarwal, Marinka Zitnik, and Himabindu Lakkaraju · 2021
Cited alongside, same era.
Robust counterfactual explanations on graph neural networks
Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, and Yong Zhang · 2021
Cited alongside, same era.
What I cannot predict, I do not understand: A human-centered evaluation framework for explainability methods
Julien Colin, Thomas Fel, Remi Cadene, and Thomas Serre · 2021
Cited alongside, same era.
OOD-GNN: Out-of-Distribution generalized graph neural network
Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu · 2021
Cited alongside, same era.
Evaluating attribution for graph neural networks
Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Peter Y Wang, Wesley Wei Qian, Kevin Mc Closkey, Lucy Colwell, and Alexander Wiltschko · 2021
Cited alongside, same era.
Generative explanation for graph neural network: Methods and evaluation
Jialin Chen, Kenza Amara, Junchi Yu, and Rex Ying
Cited in the paper.
Antonio Longa, Steve Azzolin, Gabriele Santin, Giulia Cencetti, Pietro Liò, Bruno Lepri, and Andrea Passerini · 2022
Later among the works it cites.
Clear: Generative counterfactual explanations on graphs
Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, and Jundong Li · 2022
Later among the works it cites.
Interpretable and generalizable graph learning via stochastic attention mechanism
Siqi Miao, Mia Liu, and Pan Li · 2022
Later among the works it cites.
From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai
Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert · 2022
Later among the works it cites.
NVIDIA CUDA Toolkit
NVIDIA Corporation · 2023
Closest in time.
Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji · 2023
Closest in time.