SubgraphX(model, num_hops, coef=10.0, high2low=True, num_child=12, num_rollouts=20, node_min=3, shapley_steps=100, log=False)¶
SubgraphX from On Explainability of Graph Neural Networks via Subgraph Explorations <https://arxiv.org/abs/2102.05152>
It identifies the most important subgraph from the original graph that plays a critical role in GNN-based graph classification.
It employs Monte Carlo tree search (MCTS) in efficiently exploring different subgraphs for explanation and uses Shapley values as the measure of subgraph importance.
model (nn.Module) –
The GNN model to explain that tackles multiclass graph classification
Its forward function must have the form
forward(self, graph, nfeat).
The output of its forward function is the logits.
num_hops (int) – Number of message passing layers in the model
coef (float, optional) – This hyperparameter controls the trade-off between exploration and exploitation. A higher value encourages the algorithm to explore relatively unvisited nodes. Default: 10.0
high2low (bool, optional) – If True, it will use the “High2low” strategy for pruning actions, expanding children nodes from high degree to low degree when extending the children nodes in the search tree. Otherwise, it will use the “Low2high” strategy. Default: True
num_child (int, optional) – This is the number of children nodes to expand when extending the children nodes in the search tree. Default: 12
num_rollouts (int, optional) – This is the number of rollouts for MCTS. Default: 20
node_min (int, optional) – This is the threshold to define a leaf node based on the number of nodes in a subgraph. Default: 3
shapley_steps (int, optional) – This is the number of steps for Monte Carlo sampling in estimating Shapley values. Default: 100
log (bool, optional) – If True, it will log the progress. Default: False
explain_graph(graph, feat, target_class, **kwargs)¶
Find the most important subgraph from the original graph for the model to classify the graph into the target class.
Nodes that represent the most important subgraph
- Return type
>>> import torch >>> import torch.nn as nn >>> import torch.nn.functional as F >>> from dgl.data import GINDataset >>> from dgl.dataloading import GraphDataLoader >>> from dgl.nn import GraphConv, AvgPooling, SubgraphX
>>> # Define the model >>> class Model(nn.Module): ... def __init__(self, in_dim, n_classes, hidden_dim=128): ... super().__init__() ... self.conv1 = GraphConv(in_dim, hidden_dim) ... self.conv2 = GraphConv(hidden_dim, n_classes) ... self.pool = AvgPooling() ... ... def forward(self, g, h): ... h = F.relu(self.conv1(g, h)) ... h = self.conv2(g, h) ... return self.pool(g, h)
>>> # Load dataset >>> data = GINDataset('MUTAG', self_loop=True) >>> dataloader = GraphDataLoader(data, batch_size=64, shuffle=True)
>>> # Train the model >>> feat_size = data.ndata['attr'].shape >>> model = Model(feat_size, data.gclasses) >>> criterion = nn.CrossEntropyLoss() >>> optimizer = torch.optim.Adam(model.parameters(), lr=1e-2) >>> for bg, labels in dataloader: ... logits = model(bg, bg.ndata['attr']) ... loss = criterion(logits, labels) ... optimizer.zero_grad() ... loss.backward() ... optimizer.step()
>>> # Initialize the explainer >>> explainer = SubgraphX(model, num_hops=2)
>>> # Explain the prediction for graph 0 >>> graph, l = data >>> graph_feat = graph.ndata.pop("attr") >>> g_nodes_explain = explainer.explain_graph(graph, graph_feat, ... target_class=l)
forward(*input: Any) → None¶
Defines the computation performed at every call.
Should be overridden by all subclasses.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.