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Numerous explainability methods have been proposed to shed light on the inner workings of GNNs.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Asim Kumar Debnath, Rosa L Lopez de Compadre, Gargi Debnath, Alan J Shusterman, and Corwin Hansch · 1991
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An abstract representation for molecular graphs , pp. 343–366
Philippe Vismara and Claude Laurenço · 2000
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Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig · 2003
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Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel · 2005
Earlier work this paper cites.
Derivation and validation of toxicophores for mutagenicity prediction
Jeroen Kazius, Ross McGuire, and Roberta Bursi · 2005
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Iam graph database repository for graph based pattern recognition and machine learning
Kaspar Riesen and Horst Bunke · 2008
Earlier work this paper cites.
Comparison of descriptor spaces for chemical compound retrieval and classification
Nikil Wale, Ian A Watson, and George Karypis · 2008
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, A. Perelygin, J.Y. Wu, J. Chuang, C.D. Manning, A.Y. Ng, and C. Potts · 2013
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Inductive representation learning on large graphs
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
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Modeling trajectories with recurrent neural networks
Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, and Wei Wang · 2017
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A survey of machine learning for big code and naturalness
Miltiadis Allamanis, Earl T Barr, Premkumar Devanbu, and Charles Sutton · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, and Jure Leskovec · 2018
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Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour · 2019
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Understanding isomorphism bias in graph data sets
Sergei Ivanov, Sergei Sviridov, and Evgeny Burnaev · 2019
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Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Graphgen: a scalable approach to domain-agnostic labeled graph generation
Nikhil Goyal, Harsh Vardhan Jain, and Sayan Ranu · 2020
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Spatial transition learning on road networks with deep probabilistic models
Xiucheng Li, G. Cong, and Yun Cheng · 2020
Earlier work this paper cites.
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.
Gcomb: Learning budget-constrained combinatorial algorithms over billion-sized graphs
Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu, and Ambuj Singh · 2020
Cited alongside, same era.
Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
Minh Vu and My T Thai · 2020
Cited alongside, same era.
Xgnn: Towards model-level explanations of graph neural networks
Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji · 2020
Cited alongside, same era.
Counterfactual graphs for explainable classification of brain networks
Carlo Abrate and Francesco Bonchi · 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
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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Greed: A neural framework for learning graph distance functions
Rishabh Ranjan, Siddharth Grover, Sourav Medya, Venkatesan Chakaravarthy, Yogish Sabharwal, and Sayan Ranu · 2022
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Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning
Juntao Tan, Shijie Geng, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Yunqi Li, and Yongfeng Zhang · 2022
Later among the works it cites.
Unravelling the performance of physics-informed graph neural networks for dynamical systems
Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi, Ravinder Bhattoo, NM Krishnan, and Sayan Ranu · 2022
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Model agnostic generation of counterfactual explanations for molecules
Geemi P Wellawatte, Aditi Seshadri, and Andrew D White · 2022
Later among the works it cites.
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Cited alongside, same era.
Knowledge-preserving incremental social event detection via heterogeneous gnns
Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, Jianxin Li, and Philip S Yu · 2021
Cited alongside, same era.
Neuromlr: Robust & reliable route recommendation on road networks
Jayant Jain, Vrittika Bagadia, Sahil Manchanda, and Sayan Ranu · 2021
Cited alongside, same era.
Event detection on dynamic graphs
Mert Kosan, Arlei Silva, Sourav Medya, Brian Uzzi, and Ambuj Singh · 2021
Cited alongside, same era.
Higher-order explanations of graph neural networks via relevant walks
Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T Schütt, Klaus-Robert Müller, and Grégoire Montavon · 2021
Cited alongside, same era.
Reinforcement learning enhanced explainer for graph neural networks
Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, and Dongsheng Li · 2021
Cited alongside, same era.
Adversarial attacks on graph classifiers via bayesian optimisation
Xingchen Wan, Henry Kenlay, Binxin Ru, Arno Blaas, Michael Osborne, and Xiaowen Dong · 2021
Cited alongside, same era.
Task-agnostic graph explanations
Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward Huang, Nikhil Rao, Karthik Subbian, and Shuiwang Ji · 2022
Later among the works it cites.
Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji · 2022
Later among the works it cites.
Evaluating explainability for graph neural networks
Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, and Marinka Zitnik · 2023
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Global explainability of GNNs via logic combination of learned concepts
Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Lio, and Andrea Passerini · 2023
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Learning the dynamics of particle-based systems with lagrangian graph neural networks
Ravinder Bhattoo, Sayan Ranu, and NM Anoop Krishnan · 2023
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Enhancing the inductive biases of graph neural ode for modeling dynamical systems
Suresh Bishnoi, Ravinder Bhattoo, Sayan Ranu, and NM Krishnan · 2023
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Learning and maximizing influence in social networks under capacity constraints
Pritish Chakraborty, Sayan Ranu, Krishna Sri Ipsit Mantri, and Abir De · 2023
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Cooperative explanations of graph neural networks
Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, and Tat-Seng Chua · 2023
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FRIGATE: Frugal spatio-temporal forecasting on road networks
Mridul Gupta, Hariprasad Kodamana, and Sayan Ranu · 2023
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Global counterfactual explainer for graph neural networks
Zexi Huang, Mert Kosan, Sourav Medya, Sayan Ranu, and Ambuj Singh · 2023
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A survey on explainability of graph neural networks
Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma, Charu Aggarwal, and Sourav Medya · 2023
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Robust ante-hoc graph explainer using bilevel optimization
Mert Kosan, Arlei Silva, and Ambuj Singh · 2023
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A survey on graph counterfactual explanations: Definitions, methods, evaluation, and research challenges
Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo, and Fosca Giannotti · 2023
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Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2023
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Global concept-based interpretability for graph neural networks via neuron analysis
Han Xuanyuan, Pietro Barbiero, Dobrik Georgiev, Lucie Charlotte Magister, and Pietro Lió · 2023
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