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Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years.
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Graphlime: Local interpretable model explanations for graph neural networks
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Causal Screening to Interpret Graph Neural Networks
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Explainability in graph neural networks: A taxonomic survey
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Semi-Supervised Graph-to-Graph Translation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 1863–1872
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GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks. In Proceedings of the Fourteenth ACM International Conference on Web Search and Data Mining
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Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels
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Prototypical graph contrastive learning
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Discovering Invariant Rationales for Graph Neural Networks
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Decoupled Self-supervised Learning for Non-Homophilous Graphs
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HP-GMN: Graph Memory Networks for Heterophilous Graphs
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Exploring edge disentanglement for node classification. In Proceedings of the ACM Web Conference 2022 . 1028–1036
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