Salīdzināt metodes

	Skaidrojami grafu neironu tīkli	Grafu neironu tīkls
Nozare	Dziļā mācīšanās	Tīklu analīze
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2019	2017–2018 (major variants)
Autors	Ying, Z. et al. (GNNExplainer); broader XAI-GNN field	—
Tips	Interpretability framework for graph neural networks	Deep learning on graph-structured data
Pirmavots	Ying, Z., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating Explanations for Graph Neural Networks. Advances in Neural Information Processing Systems (NeurIPS), 32, 9240–9251.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).
Citi nosaukumi	XAI-GNN, GNN explainability, interpretable GNN, explainable GNN	GNN, GCN, GAT, GraphSAGE
Saistītās	3	5
Kopsavilkums	Explainable Graph Neural Networks (XAI-GNN) combine standard GNN architectures with post-hoc or intrinsic explanation techniques that reveal which nodes, edges, and node features drove a model's prediction. Pioneered by GNNExplainer (Ying et al., 2019), the field addresses the black-box critique of GNNs and is essential wherever graph-based predictions must be trusted or audited.	A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes.