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Evaluating and improving explanation quality of graph neural network link prediction on knowledge graphs. (Évaluation et amélioration de la qualité d'explication de la prédiction des liens par réseau neuronal sur les graphes de connaissances).. Côte d'Azur University, Nice, France, (2022)Linked Data Ground Truth for Quantitative and Qualitative Evaluation of Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs., , and . WI/IAT, page 178-185. ACM, (2021)Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach., and . CoRR, (2020)User Scored Evaluation of Non-Unique Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs., , and . K-CAP, page 57-64. ACM, (2021)Evaluating Explanations of Relational Graph Convolutional Network Link Predictions on Knowledge Graphs.. AAAI, page 12880-12881. AAAI Press, (2022)A Simplified Benchmark for Non-ambiguous Explanations of Knowledge Graph Link Prediction using Relational Graph Convolutional Networks., , and . ISWC (Posters/Demos/Industry), volume 2980 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction Using Relational Graph Convolutional Networks (Student Abstract)., , and . AAAI, page 12963-12964. AAAI Press, (2022)Impact of Injecting Ground Truth Explanations on Relational Graph Convolutional Networks and their Explanation Methods for Link Prediction on Knowledge Graphs., , and . WI/IAT, page 288-295. IEEE, (2022)