dig.xgraph.method¶
Methods interfaces under dig.xgraph.method
.
An implementation of DeepLIFT on graph in Learning Important Features Through Propagating Activation Differences. :param model: The target model prepared to explain. :type model: torch.nn.Module :param explain_graph: Whether to explain graph classification model. (default: 

The GNNExplainer model from the "GNNExplainer: Generating Explanations for Graph Neural Networks" paper for identifying compact subgraph structures and small subsets node features that play a crucial role in a GNN’s nodepredictions. .. note:: For an example, see benchmarks/xgraph. :param model: The GNN module to explain. :type model: torch.nn.Module :param epochs: The number of epochs to train. (default: 

An implementation of GNNGI in HigherOrder Explanations of Graph Neural Networks via Relevant Walks. 

An implementation of GNNLRP in HigherOrder Explanations of Graph Neural Networks via Relevant Walks. :param model: The target model prepared to explain. :type model: torch.nn.Module :param explain_graph: Whether to explain graph classification model. (default: 

An implementation of GradCAM on graph in GradCAM: Visual Explanations from Deep Networks via Gradientbased Localization. 

An implementation of PGExplainer in Parameterized Explainer for Graph Neural Network. 

The implementation of paper On Explainability of Graph Neural Networks via Subgraph Explorations. 
 class DeepLIFT(model: Module, explain_graph: bool = False)[source]¶
An implementation of DeepLIFT on graph in Learning Important Features Through Propagating Activation Differences. :param model: The target model prepared to explain. :type model: torch.nn.Module :param explain_graph: Whether to explain graph classification model.
(default:
False
)Note
For node classification model, the
explain_graph
flag is False. For an example, see benchmarks/xgraph. forward(x: Tensor, edge_index: Tensor, **kwargs)[source]¶
Run the explainer for a specific graph instance. :param x: The graph instance’s input node features. :type x: torch.Tensor :param edge_index: The graph instance’s edge index. :type edge_index: torch.Tensor :param **kwargs:
node_idx
（int): The index of node that is pending to be explained.(for node classification)
sparsity
(float): The Sparsity we need to control to transform a soft mask to a hard mask. (Default:0.7
)Note
(None, edge_masks, related_predictions): edge_masks is a list of edgelevel explanation for each class; related_predictions is a list of dictionary for each class where each dictionary includes 4 type predicted probabilities.
 class GNNExplainer(model: Module, epochs: int = 100, lr: float = 0.01, coff_edge_size: float = 0.001, coff_edge_ent: float = 0.001, coff_node_feat_size: float = 1.0, coff_node_feat_ent: float = 0.1, explain_graph: bool = False)[source]¶
The GNNExplainer model from the “GNNExplainer: Generating Explanations for Graph Neural Networks” paper for identifying compact subgraph structures and small subsets node features that play a crucial role in a GNN’s nodepredictions. .. note:: For an example, see `benchmarks/xgraph
 Parameters
model (torch.nn.Module) – The GNN module to explain.
epochs (int, optional) – The number of epochs to train. (default:
100
)lr (float, optional) – The learning rate to apply. (default:
0.01
)explain_graph (bool, optional) – Whether to explain graph classification model (default:
False
)
 forward(x, edge_index, mask_features=False, target_label=None, **kwargs)[source]¶
Run the explainer for a specific graph instance. :param x: The graph instance’s input node features. :type x: torch.Tensor :param edge_index: The graph instance’s edge index. :type edge_index: torch.Tensor :param mask_features: Whether to use feature mask. Not recommended.
(Default:
False
) Parameters
target_label (torch.Tensor, optional) – if given then apply optimization only on this label
**kwargs (dict) –
node_idx
（int, list, tuple, torch.Tensor): The index of node that is pending to be explained. (for node classification)sparsity
(float): The Sparsity we need to control to transform a soft mask to a hard mask. (Default:0.7
)num_classes
(int): The number of task’s classes.
 Return type
Note
(None, edge_masks, related_predictions): edge_masks is a list of edgelevel explanation for each class; related_predictions is a list of dictionary for each class where each dictionary includes 4 type predicted probabilities.
 class GNN_GI(model: Module, explain_graph: bool = False)[source]¶
An implementation of GNNGI in HigherOrder Explanations of Graph Neural Networks via Relevant Walks.
 Parameters
model (torch.nn.Module) – The target model prepared to explain.
explain_graph (bool, optional) – Whether to explain graph classification model. (default:
False
)
Note
For node classification model, the
explain_graph
flag is False. forward(x: Tensor, edge_index: Tensor, **kwargs)[source]¶
Run the explainer for a specific graph instance.
 Parameters
x (torch.Tensor) – The graph instance’s input node features.
edge_index (torch.Tensor) – The graph instance’s edge index.
**kwargs (dict) –
node_idx
（int): The index of node that is pending to be explained. (for node classification)sparsity
(float): The Sparsity we need to control to transform a soft mask to a hard mask. (Default:0.7
)num_classes
(int): The number of task’s classes.
 Return type
Note
(walks, edge_masks, related_predictions): walks is a dictionary including walks’ edge indices and corresponding explained scores; edge_masks is a list of edgelevel explanation for each class; related_predictions is a list of dictionary for each class where each dictionary includes 4 type predicted probabilities.
 class GNN_LRP(model: Module, explain_graph=False)[source]¶
An implementation of GNNLRP in HigherOrder Explanations of Graph Neural Networks via Relevant Walks. :param model: The target model prepared to explain. :type model: torch.nn.Module :param explain_graph: Whether to explain graph classification model.
(default:
False
)Note
For node classification model, the
explain_graph
flag is False. GNNLRP is very model dependent. Please be sure you know how to modify it for different models. For an example, see benchmarks/xgraph. forward(x: Tensor, edge_index: Tensor, **kwargs)[source]¶
Run the explainer for a specific graph instance. :param x: The graph instance’s input node features. :type x: torch.Tensor :param edge_index: The graph instance’s edge index. :type edge_index: torch.Tensor :param **kwargs:
node_idx
（int): The index of node that is pending to be explained.(for node classification)
sparsity
(float): The Sparsity we need to control to transform a soft mask to a hard mask. (Default:0.7
)num_classes
(int): The number of task’s classes. Return type
(walks, edge_masks, related_predictions), walks is a dictionary including walks’ edge indices and corresponding explained scores; edge_masks is a list of edgelevel explanation for each class; related_predictions is a list of dictionary for each class where each dictionary includes 4 type predicted probabilities.
 class GradCAM(model: Module, explain_graph: bool = False)[source]¶
An implementation of GradCAM on graph in GradCAM: Visual Explanations from Deep Networks via Gradientbased Localization.
 Parameters
model (torch.nn.Module) – The target model prepared to explain.
explain_graph (bool, optional) – Whether to explain graph classification model. (default:
False
)
Note
For node classification model, the
explain_graph
flag is False. For an example, see benchmarks/xgraph. forward(x: Tensor, edge_index: Tensor, **kwargs) Union[Tuple[None, List, List[Dict]], Tuple[List, List, List[Dict]]] [source]¶
Run the explainer for a specific graph instance.
 Parameters
x (torch.Tensor) – The graph instance’s input node features.
edge_index (torch.Tensor) – The graph instance’s edge index.
**kwargs (dict) –
node_idx
（int): The index of node that is pending to be explained. (for node classification)sparsity
(float): The Sparsity we need to control to transform a soft mask to a hard mask. (Default:0.7
)num_classes
(int): The number of task’s classes.
 Return type
Note
(None, edge_masks, related_predictions): edge_masks is a list of edgelevel explanation for each class; related_predictions is a list of dictionary for each class where each dictionary includes 4 type predicted probabilities.
 class PGExplainer(model, in_channels: int, device, explain_graph: bool = True, epochs: int = 20, lr: float = 0.005, coff_size: float = 0.01, coff_ent: float = 0.0005, t0: float = 5.0, t1: float = 1.0, sample_bias: float = 0.0, num_hops: Optional[int] = None)[source]¶
An implementation of PGExplainer in Parameterized Explainer for Graph Neural Network.
 Parameters
model (
torch.nn.Module
) – The target model prepared to explainin_channels (
int
) – Number of input channels for the explanation networkexplain_graph (
bool
) – Whether to explain graph classification model (default:True
)epochs (
int
) – Number of epochs to train the explanation networklr (
float
) – Learning rate to train the explanation networkcoff_size (
float
) – Size regularization to constrain the explanation sizecoff_ent (
float
) – Entropy regularization to constrain the connectivity of explanationt0 (
float
) – The temperature at the first epocht1 (
float
) – The temperature at the final epochnum_hops (
int
,None
) – The number of hops to extract neighborhood of target node(default –
None
)
 __set_masks__(x: Tensor, edge_index: Tensor, edge_mask: Optional[Tensor] = None)[source]¶
Set the edge weights before message passing
 Parameters
x (
torch.Tensor
) – Node feature matrix with shape[num_nodes, dim_node_feature]
edge_index (
torch.Tensor
) – Graph connectivity in COO format with shape[2, num_edges]
edge_mask (
torch.Tensor
) – Edge weight matrix before message passing (default:None
)
The
edge_mask
will be randomly initialized when set toNone
.Note
When you use the
__set_masks__()
, the explain flag for all thetorch_geometric.nn.MessagePassing
modules inmodel
will be assigned withTrue
. In addition, theedge_mask
will be assigned to all the modules. Please take__clear_masks__()
to reset.
 concrete_sample(log_alpha: Tensor, beta: float = 1.0, training: bool = True)[source]¶
Sample from the instantiation of concrete distribution when training
 explain(x: Tensor, edge_index: Tensor, embed: Tensor, tmp: float = 1.0, training: bool = False, **kwargs) Tuple[float, Tensor] [source]¶
explain the GNN behavior for graph with explanation network
 Parameters
x (
torch.Tensor
) – Node feature matrix with shape[num_nodes, dim_node_feature]
edge_index (
torch.Tensor
) – Graph connectivity in COO format with shape[2, num_edges]
embed (
torch.Tensor
) – Node embedding matrix with shape[num_nodes, dim_embedding]
( (tmp) – obj`float`): The temperature parameter fed to the sample procedure
training (
bool
) – Whether in training procedure or not
 Returns
The classification probability for graph with edge mask edge_mask (
torch.Tensor
): The probability mask for graph edges Return type
probs (
torch.Tensor
)
 forward(x: Tensor, edge_index: Tensor, **kwargs) Tuple[None, List, List[Dict]] [source]¶
explain the GNN behavior for graph and calculate the metric values. The interface for the
dig.evaluation.XCollector
. Parameters
x (
torch.Tensor
) – Node feature matrix with shape[num_nodes, dim_node_feature]
edge_index (
torch.Tensor
) – Graph connectivity in COO format with shape[2, num_edges]
kwargs (
Dict
) – The additional parameters
top_k (
int
): The number of edges in the final explanation results
 Return type
(
None
, List[torch.Tensor], List[Dict])
 get_subgraph(node_idx: int, x: Tensor, edge_index: Tensor, y: Optional[Tensor] = None, **kwargs) Tuple[Tensor, Tensor, Tensor, List, Dict] [source]¶
extract the subgraph of target node
 Parameters
node_idx (
int
) – The node indexx (
torch.Tensor
) – Node feature matrix with shape[num_nodes, dim_node_feature]
edge_index (
torch.Tensor
) – Graph connectivity in COO format with shape[2, num_edges]
y (
torch.Tensor
,None
) – Node label matrix with shape[num_nodes]
(defaultNone
)kwargs (
Dict
,None
) – Additional parameters
 Return type
(
torch.Tensor
,torch.Tensor
,torch.Tensor
,List
,Dict
)
 class SubgraphX(model, num_classes: int, device, num_hops: Optional[int] = None, verbose: bool = False, explain_graph: bool = True, rollout: int = 20, min_atoms: int = 5, c_puct: float = 10.0, expand_atoms=14, high2low=False, local_radius=4, sample_num=100, reward_method='mc_l_shapley', subgraph_building_method='zero_filling', save_dir: Optional[str] = None, filename: str = 'example', vis: bool = True)[source]¶
The implementation of paper On Explainability of Graph Neural Networks via Subgraph Explorations.
 Parameters
model (
torch.nn.Module
) – The target model prepared to explainnum_classes (
int
) – Number of classes for the datasetsnum_hops (
int
,None
) – The number of hops to extract neighborhood of target node (default:None
)explain_graph (
bool
) – Whether to explain graph classification model (default:True
)rollout (
int
) – Number of iteration to get the predictionmin_atoms (
int
) – Number of atoms of the leaf node in search treec_puct (
float
) – The hyperparameter which encourages the explorationexpand_atoms (
int
) – The number of atoms to expand when extend the child nodes in the search treehigh2low (
bool
) – Whether to expand children nodes from high degree to low degree when extend the child nodes in the search tree (default:False
)local_radius (
int
) – Number of local radius to calculatel_shapley
,mc_l_shapley
sample_num (
int
) – Sampling time of monte carlo sampling approximation formc_shapley
,mc_l_shapley
(default:mc_l_shapley
)reward_method (
str
) – The command string to select thesubgraph_building_method (
str
) – The command string for different subgraph building method, such aszero_filling
,split
(default:zero_filling
)save_dir (
str
,None
) – Root directory to save the explanation results (default:None
)filename (
str
) – The filename of resultsvis (
bool
) – Whether to show the visualization (default:True
)
Example
>>> # For graph classification task >>> subgraphx = SubgraphX(model=model, num_classes=2) >>> _, explanation_results, related_preds = subgraphx(x, edge_index)
 __call__(x: Tensor, edge_index: Tensor, **kwargs) Tuple[None, List, List[Dict]] [source]¶
explain the GNN behavior for the graph using SubgraphX method :param x: Node feature matrix with shape
[num_nodes, dim_node_feature]
 Parameters
edge_index (
torch.Tensor
) – Graph connectivity in COO format with shape[2, num_edges]
kwargs (
Dict
) –
 Return type
(
None
, List[torch.Tensor], List[Dict])