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Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI systems.
Mathematical Tools for Data Mining-Set Theory, Partial Orders, Combinatorics
Dan, A. S.; and Djeraba, C. 2008 · 2008
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The graph neural network model
Scarselli, F.; Gori, M.; Tsoi, A.; Hagenbuchner, M.; and Monfardini, G. 2008 · 2008
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The graph matching problem
Livi, L.; and Rizzi, A. 2013 · 2013
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Variational graph auto-encoders
Kipf, T. N.; and Welling, M. 2016 · 2016
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Deep model for dropout prediction in MOOCs
Wang, W.; Yu, H.; and Miao, C. 2017 · 2017
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A survey of methods for explaining black box models
Guidotti, R.; Monreale, A.; Ruggieri, S.; Turini, F.; Giannotti, F.; and Pedreschi, D. 2018 · 2018
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Effective feature learning with unsupervised learning for improving the predictive models in massive open online courses
Ding, M.; Yang, K.; Yeung, D.-Y.; and Pong, T.-C. 2019 · 2019
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Understanding dropouts in MOOCs
Feng, W.; Tang, J.; and Liu, T. X. 2019 · 2019
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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View
Loyola-González, O. 2019 · 2019
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Predicting process performance: A white-box approach based on process models
Verenich, I.; Dumas, M.; La Rosa, M.; and Nguyen, H. 2019 · 2019
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Session-based recommendation with graph neural networks
Wu, S.; Tang, Y.; Zhu, Y.; Wang, L.; Xie, X.; and Tan, T. 2019 · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Ying, Z.; Bourgeois, D.; You, J.; Zitnik, M.; and Leskovec, J. 2019 · 2019
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SkipGNN: predicting molecular interactions with skip-graph networks
Huang, K.; Xiao, C.; Glass, L. M.; Zitnik, M.; and Sun, J. 2020 · 2020
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A feature-learning-based method for the disease-gene prediction problem
Madeddu, L.; Stilo, G.; and Velardi, P. 2020 · 2020
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A reproducibility study of deep and surface machine learning methods for human-related trajectory prediction
Prenkaj, B.; Velardi, P.; Distante, D.; and Faralli, S. 2020 · 2020
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Ris-gan: Explore residual and illumination with generative adversarial networks for shadow removal
Zhang, L.; Long, C.; Zhang, X.; and Xiao, C. 2020 · 2020
Cited alongside, same era.
Counterfactual graphs for explainable classification of brain networks
Abrate, C.; and Bonchi, F. 2021 · 2021
Cited alongside, same era.
CoRoNNa: a deep sequential framework to predict epidemic spread
Aragona, D.; Podo, L.; Prenkaj, B.; and Velardi, P. 2021 · 2021
Cited alongside, same era.
Robust counterfactual explanations on graph neural networks
Bajaj, M.; Chu, L.; Xue, Z. Y.; Pei, J.; Wang, L.; Lam, P. C.-H.; and Zhang, Y. 2021 · 2021
Cited alongside, same era.
Multi-objective Explanations of GNN Predictions
Liu, Y.; Chen, C.; Liu, Y.; Zhang, X.; and Xie, S. 2021 · 2021
Cited alongside, same era.
Meg: Generating molecular counterfactual explanations for deep graph networks
Explaining Black Box Drug Target Prediction through Model Agnostic Counterfactual Samples
Nguyen, T. M.; Quinn, T. P.; Nguyen, T.; and Tran, T. 2022 · 2022
Later among the works it cites.
Gretel: Graph counterfactual explanation evaluation framework
Prado-Romero, M. A.; and Stilo, G. 2022 · 2022
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Learning and Evaluating Graph Neural Network Explanations Based on Counterfactual and Factual Reasoning
Tan, J.; Geng, S.; Fu, Z.; Ge, Y.; Xu, S.; Li, Y.; and Zhang, Y. 2022 · 2022
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Temporal deep learning architecture for prediction of COVID-19 cases in India
Verma, H.; Mandal, S.; and Gupta, A. 2022 · 2022
Later among the works it cites.
Explainable image classification with evidence counterfactual
Vermeire, T.; Brughmans, D.; Goethals, S.; de Oliveira, R. M. B.; and Martens, D. 2022 · 2022
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Numeroso, D.; and Bacciu, D. 2021 · 2021
Cited alongside, same era.
Opening the black box: the promise and limitations of explainable machine learning in cardiology
Petch, J.; Di, S.; and Nelson, W. 2021 · 2021
Cited alongside, same era.
Hidden space deep sequential risk prediction on student trajectories
Prenkaj, B.; Distante, D.; Faralli, S.; and Velardi, P. 2021 · 2021
Cited alongside, same era.
Session-based Recommendation with Heterogeneous Graph Neural Networks
Xu, L.; Xi, W.; and Wang, C. 2021 · 2021
Cited alongside, same era.
How Powerful are K-hop Message Passing Graph Neural Networks
Feng, J.; Chen, Y.; Li, F.; Sarkar, A.; and Zhang, M. 2022 · 2022
Cited alongside, same era.
Counterfactual explanations and how to find them: literature review and benchmarking
Guidotti, R. 2022 · 2022
Cited alongside, same era.
VCNet: A self-explaining model for realistic counterfactual generation
Guyomard, V.; Fessant, F.; Guyet, T.; Bouadi, T.; and Termier, A. 2022 · 2022
Cited alongside, same era.
Wei, X.; Liu, Y.; Sun, J.; Jiang, Y.; Tang, Q.; and Yuan, K. 2022 · 2022
Later among the works it cites.
Model agnostic generation of counterfactual explanations for molecules
Wellawatte, G. P.; Seshadri, A.; and White, A. D. 2022 · 2022
Later among the works it cites.
Explainability in graph neural networks: A taxonomic survey
Yuan, H.; Yu, H.; Gui, S.; and Ji, S. 2022 · 2022
Later among the works it cites.
Global counterfactual explainer for graph neural networks
Huang, Z.; Kosan, M.; Medya, S.; Ranu, S.; and Singh, A. 2023 · 2023
Closest in time.
Revisiting CounteRGAN for Counterfactual Explainability of Graphs
Prado-Romero, M. A.; Prenkaj, B.; and Stilo, G. 2023 · 2023
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A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges
Prado-Romero, M. A.; Prenkaj, B.; Stilo, G.; and Giannotti, F. 2023 · 2023
Closest in time.
Counterfactual Editing for Search Result Explanation
Xu, Z.; Lamba, H.; Ai, Q.; Tetreault, J.; and Jaimes, A. 2023 · 2023
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OCTET: Object-Aware Counterfactual Explanations
Zemni, M.; Chen, M.; Zablocki, E.; Ben-Younes, H.; Pérez, P.; and Cord, M. 2023 · 2023
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Clare: A semi-supervised community detection algorithm
Wu, X.; Xiong, Y.; Zhang, Y.; Jiao, Y.; Shan, C.; Sun, Y.; Zhu, Y.; and Yu, P. S. 2022 · 2069
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