Feb 3, 2024 · The paper presents a novel method, V-InFoR, to explain Graph Neural Networks (GNNs) in the presence of structural corruptions in graphs.
We propose a robust GNN explainer called V-InfoR. Specifically, a robust graph representation extractor, which takes insights of variational inference, is ...
This paper for the first time investigates how to construct a robust GNN explainer for structurally corrupted graphs. As illustrated in Figure 1, based on ...
Oct 1, 2023 · GNN explanation method aims to identify an explanatory subgraph which contains the most informative components of the full graph.
May 30, 2024 · GNN explanation method aims to identify an explanatory subgraph which contains the most informative components of the full graph.
... Graph Neural Networks via Motif Discovery [paper]; [NeurIPS 23] V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs [paper] ...
Jun 10, 2024 · V-InFoR: A Robust Graph. Neural Networks Explainer for Structurally Corrupted Graphs. Advances in Neural Information. Processing Systems 36 ( ...
Sep 24, 2024 · The first work designs a novel GNN explainer named V-InfoR to provide a more robust GNN explanation for the structurally corrupted graphs.
V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs ... Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach ...
V-InFoR: a robust graph neural networks explainer for structurally corrupted graphs. S Wang, J Yin, C Li, X Xie, J Wang. Advances in Neural Information ...
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