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Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models.
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F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, and M. M. Bronstein, “Geometric deep learning on graphs and manifolds using mixture model CNNs,” in
2017
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2017
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K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in
2019
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F. Baldassarre and H. Azizpour, “Explainability techniques for graph convolutional networks,” in
2019
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Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec, “GNNExplainer: Generating explanations for graph neural networks,” in
2019
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N. Sammaknejad, Y. Zhao, and B. Huang, “A review of the expectation maximization algorithm in data-driven process identification,”
2019
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2019
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2021
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2021
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2021
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2021
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T. Schnake, O. Eberle, J. Lederer, S. Nakajima, K. T. Schutt, K. Mueller, and G. Montavon, “Higher-order explanations of graph neural networks via relevant walks,”
2022
Closest in time.
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2022
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2022
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2022
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2023
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2023
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