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Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data.
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2017
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2019
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Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–21, 2020
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A. B. Arrieta, N. D. Rodríguez, J. D. Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, and F. Herrera, “Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI,” Inf. Fusion , vol. 58, pp. 82–115, 2020
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H. Yuan, J. Tang, X. Hu, and S. Ji, “XGNN: towards model-level explanations of graph neural networks,” in The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , R. Gupta, Y. Liu, J. Tang, and B. A. Prakash, Eds. ACM, 2020, pp. 430–438
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