We define a network that is to be trained and explained by GNNExplainer. We construct a simple graph neural network composed of 2 graph convolution layers(Kipf ...

GNNExplainer: Generating Explanations for Graph Neural Networks. Advances in Neural Information Processing Systems 32. 2019.

To evaluate the explanation from the GNNExplainer , we can utilize the torch_geometric.explain.metric module. For example, to compute the unfaithfulness() of an ...

17 янв. 2024 г. · GNNExplainer is an approach for explaining predictions made by GNNs. GNNExplainer takes a trained GNN and its prediction and it returns an explanation.

The GNN-Explainer model from the “GNNExplainer: Generating Explanations for Graph Neural Networks” paper for identifying compact subgraph structures and node ...

Here we propose GNNExplainer, the first general, model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any ...

2 февр. 2023 г. · In this blog post we will go step by step through the explainability module, shedding light on how each component of the framework works and what purpose it ...

- GNN is learning a conditional distribution PΦ. (Y | Gc. , Xc. ), where Y is a random variable representing the label in {1, …, C}. - Gs is a subgraph of the ...

Learn and return a node feature mask and an edge mask that play a crucial role to explain the prediction made by the GNN for a graph.

10 мая 2020 г. · The primary objective for GNNExplainer is to generate a minimal graph that explains the decision for a node or a graph.