class GNNExplainer(epochs: int = 100, lr: float = 0.01, **kwargs)[source]

Bases: ExplainerAlgorithm

The GNN-Explainer model from the “GNNExplainer: Generating Explanations for Graph Neural Networks” paper for identifying compact subgraph structures and node features that play a crucial role in the predictions made by a GNN.


The edge_size coefficient is multiplied by the number of nodes in the explanation at every iteration, and the resulting value is added to the loss as a regularization term, with the goal of producing compact explanations. A higher value will push the algorithm towards explanations with less elements. Consider adjusting the edge_size coefficient according to the average node degree in the dataset, especially if this value is bigger than in the datasets used in the original paper.

  • epochs (int, optional) – The number of epochs to train. (default: 100)

  • lr (float, optional) – The learning rate to apply. (default: 0.01)

  • **kwargs (optional) – Additional hyper-parameters to override default settings in coeffs.

forward(model: Module, x: Tensor, edge_index: Tensor, *, target: Tensor, index: Optional[Union[int, Tensor]] = None, **kwargs) Explanation[source]

Computes the explanation.

  • model (torch.nn.Module) – The model to explain.

  • x (Union[torch.Tensor, Dict[NodeType, torch.Tensor]]) – The input node features of a homogeneous or heterogeneous graph.

  • edge_index (Union[torch.Tensor, Dict[NodeType, torch.Tensor]]) – The input edge indices of a homogeneous or heterogeneous graph.

  • target (torch.Tensor) – The target of the model.

  • index (Union[int, Tensor], optional) – The index of the model output to explain. Can be a single index or a tensor of indices. (default: None)

  • **kwargs (optional) – Additional keyword arguments passed to model.

supports() bool[source]

Checks if the explainer supports the user-defined settings provided in self.explainer_config, self.model_config.