Graph Machine Learning Explainability with PyG

Graph Neural Networks (GNNs) have become increasingly popular for processing graph-structured data, such as social networks, molecular graphs, and knowledge graphs. However, the complex nature of graph-based data and the non-linear relationships between nodes in a graph can make it difficult to understand why a GNN makes a particular prediction. With the rise in popularity of Graph Neural Networks, there also came an increased interest in explaining their predictions.

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