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Explaining the decisions made by machine learning models for high-stakes applications is critical for increasing transparency and guiding improvements to these decisions.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
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Derivation and validation of toxicophores for mutagenicity prediction
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An overview of bilevel optimization
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Chembl: a large-scale bioactivity database for drug discovery
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Semi-supervised classification with graph convolutional networks
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Neural message passing for quantum chemistry
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil · 2018
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Moleculenet: a benchmark for molecular machine learning
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Hierarchical graph representation learning with differentiable pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
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An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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Learning discrete structures for graph neural networks
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Generalized inner loop meta-learning
Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, and Soumith Chintala · 2019
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Semi-supervised graph classification: A hierarchical graph perspective
Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Contrastive graph neural network explanation
Lukas Faber, Amin K Moghaddam, and Roger Wattenhofer · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
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Graph meta learning via local subgraphs
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Discovering invariant rationales for graph neural networks
Yingxin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua · 2021
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Provably faster algorithms for bilevel optimization
Junjie Yang, Kaiyi Ji, and Yingbin Liang · 2021
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Graph information bottleneck for subgraph recognition
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On explainability of graph neural networks via subgraph explorations
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Deep graph structure learning for robust representations: A survey
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Qiang Liu, Shu Wu, and Liang Wang · 2021
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Parameterized explainer for graph neural network
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Rethinking pooling in graph neural networks
Diego Mesquita, Amauri Souza, and Samuel Kaski · 2020
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Interpretable machine learning
Christoph Molnar · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Explainable artificial intelligence: a systematic review
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Accurate learning of graph representations with graph multiset pooling
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Interpretable and generalizable graph learning via stochastic attention mechanism
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Graph structure learning with variational information bottleneck
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Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning
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Task-agnostic graph explanations
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Explainability in graph neural networks: A taxonomic survey
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Protgnn: Towards self-explaining graph neural networks
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Cheekong Lee · 2022
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Evaluating explainability for graph neural networks
Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, and Marinka Zitnik · 2023
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A single-loop algorithm for decentralized bilevel optimization
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Gnnx-bench: Unravelling the utility of perturbation-based gnn explainers through in-depth benchmarking
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