Is your feature request related to a problem? Please describe.
GNNExplainer is an explainability layer present in PyG, and a reference in terms of explainability of GRL algorithms.
Describe the Feature
Reference: GNNExplainer: Generating Explanations for Graph Neural Networks
GNNExplainer is the first general, model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task. Given an instance, GNNExplainer identifies a compact subgraph structure and a small subset of node features that have a crucial role in GNN's prediction. Further, GNNExplainer can generate consistent and concise explanations for an entire class of instances. We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures. Experiments on synthetic and real-world graphs show that our approach can identify important graph structures as well as node features, and outperforms baselines by 17.1% on average. GNNExplainer provides a variety of benefits, from the ability to visualize semantically relevant structures to interpretability, to giving insights into errors of faulty GNNs.
Additional context
Priority: High
Complexity: Medium
Is your feature request related to a problem? Please describe.
GNNExplainer is an explainability layer present in PyG, and a reference in terms of explainability of GRL algorithms.
Describe the Feature
Reference: GNNExplainer: Generating Explanations for Graph Neural Networks
GNNExplainer is the first general, model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task. Given an instance, GNNExplainer identifies a compact subgraph structure and a small subset of node features that have a crucial role in GNN's prediction. Further, GNNExplainer can generate consistent and concise explanations for an entire class of instances. We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures. Experiments on synthetic and real-world graphs show that our approach can identify important graph structures as well as node features, and outperforms baselines by 17.1% on average. GNNExplainer provides a variety of benefits, from the ability to visualize semantically relevant structures to interpretability, to giving insights into errors of faulty GNNs.
Additional context
Priority: High
Complexity: Medium