torch_geometric.explain.algorithm.GraphMaskExplainer

class GraphMaskExplainer(num_layers: int, epochs: int = 100, lr: float = 0.01, penalty_scaling: int = 5, lambda_optimizer_lr: int = 0.01, init_lambda: int = 0.55, allowance: int = 0.03, allow_multiple_explanations: bool = False, log: bool = True, **kwargs)[source]

Bases: ExplainerAlgorithm

The GraphMask-Explainer model from the “Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking” paper for identifying layer-wise compact subgraph structures and node features that play a crucial role in the predictions made by a GNN.

Note

For an example of using GraphMaskExplainer, see examples/explain/graphmask_explainer.py.

A working real-time example of GraphMaskExplainer in the form of a deployed app can be accessed here.

Parameters:

  • num_layers (int) – The number of layers to use.

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

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

  • penalty_scaling (int, optional) – Scaling value of penalty term. Value must lie between 0 and 10. (default: 5)

  • lambda_optimizer_lr (float, optional) – The learning rate to optimize the Lagrange multiplier. (default: 1e-2)

  • init_lambda (float, optional) – The Lagrange multiplier. Value must lie between 0 and 1. (default: 0.55)

  • allowance (float, optional) – A float value between 0 and 1 denotes tolerance level. (default: 0.03)

  • log (bool, optional) – If set to False, will not log any learning progress. (default: True)

  • **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.

Parameters:

  • 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.

reset_parameters(input_dims: List[int], h_dim: List[int])[source]

Resets all learnable parameters of the module.