# Tutorial for GNN Explainability¶

In this tutorial, we will show how to explain GNN models using our DIG library 1. Specifically, we show how to implement SubgraphX 2 to provide subgraph explanations to understand deep graph models.

## The Explainability of GNNs¶

Deep graph models are usually treated as black-boxes and lacking explainability. Without reasoning the prediction procedures of GNNs, we do not understand GNN models and do not know whether the models work in our expected way, thus preventing their use in critical applications pertaining to fairness, privacy, and safety. To demystify such black-boxes, we need to study the explainability of GNNs. Recently, several approaches are proposed to explain GNN models, such as XGNN 3, GNNExplainer 4, PGExplainer 5, and SubgraphX.

To explain GNNs, we first need to know what type of explanations we need. If we need the general understanding and high-level insights of the GNNs, we may choose to study model-level explanations. Existing methods, such as XGNN, explain what graph patterns lead to a certain behavior of the model. In addition, if we need to study explanations for individual predictions, we need to choose instance-level methods, such as GNNExplainer, PGExplainer, and SubgraphX, etc. In addition, for instance-level methods, we may select different explanation targets, such as graph edges, nodes, walks, or subgraphs.

If we study graph nodes, we can use ZORRO 6, RelEx 7, and PGM-Explainer 8.

If we study graph edges, we can use GNNExplainer, PGExplainer, GraphMask 9, and Causal Screening 10.

If we study graph walks, we can use GNN-LRP 11.

If we study subgraphs, we can use SubgraphX.

Once we decide what type of explanations and what explanation targets we need, we can start explaining deep graph models. Next, we will show how to use SubgraphX to provide subgraph explanations to explain deep graph models.

## Code Example¶

First, we load the dataset BA_shapes. It is a synthetic dataset built for the node classification task. For each graph, it consists of a base Barabási-Albert graph (300 nodes) and a house-like five-node motif. Each node is labeled based on whether it belongs to the base graph or the different spatial locations of the motif.

```
import torch
import os.path as osp
from dig.xgraph.dataset import SynGraphDataset
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
dataset = SynGraphDataset('./datasets', 'BA_shapes')
dataset.data.x = dataset.data.x.to(torch.float32)
dataset.data.x = dataset.data.x[:, :1]
dim_node = dataset.num_node_features
dim_edge = dataset.num_edge_features
num_classes = dataset.num_classes
```

Next, we load the trained GNN model. The model is trained using the BA_shapes dataset and we try to explain its predictions. Note that during the explanation stage, the target GNN model is fixed.

```
from dig.xgraph.utils.compatibility import compatible_state_dict
def check_checkpoints(root='./'):
if osp.exists(osp.join(root, 'checkpoints')):
return
url = ('https://github.com/divelab/DIG_storage/raw/main/xgraph/checkpoints.zip')
path = download_url(url, root)
extract_zip(path, root)
os.unlink(path)
from dig.xgraph.models import GCN_2l
model = GCN_2l(model_level='node', dim_node=dim_node, dim_hidden=300, num_classes=num_classes)
model.to(device)
check_checkpoints()
ckpt_path = osp.join('checkpoints', 'ba_shapes', 'GCN_2l', '0', 'GCN_2l_best.ckpt')
state_dict = compatible_state_dict(torch.load(ckpt_path, map_location='cpu')['state_dict'])
model.load_state_dict(state_dict)
```

Then given an input graph, we feed it to the GNN model and obtain the prediction for a target node. Our goal is to explain what subgraph is important for the prediction of target node. In this example, the prediction and label are both 2. Therefore, we are explaining a correct prediction.

```
data = dataset[0].to(device)
node_indices = torch.where(dataset[0].test_mask * dataset[0].y != 0)[0].tolist()
node_idx = node_indices[20] # node_idx: 515
logits = model(data.x, data.edge_index)
prediction = logits[node_idx].argmax(dim=-1)
# prediction class is 2, the middle node of the house motif
```

Since the graph model is a two-layer GNN model, the information only aggregates within 2-hops. Then for any node, its prediction is only determined by its 2-hop neighbors. Let’s visualize the target node and its 2-hop neighbors as an illustration. Here the target node is the larger blue node and different colors denote different node labels.

```
from dig.xgraph.method.subgraphx import PlotUtils
from dig.xgraph.method.subgraphx import MCTS
from torch_geometric.utils import to_networkx
subgraph_x, subgraph_edge_index, subset, edge_mask, kwargs = \
MCTS.__subgraph__(node_idx, data.x, data.edge_index, num_hops=2)
subgraph_y = data.y[subset].to('cpu')
vis_graph = to_networkx(Data(x=subgraph_x, edge_index=subgraph_edge_index))
plotutils = PlotUtils(dataset_name='ba_shapes')
plotutils.plot(vis_graph, nodelist=[], figname=None, y=subgraph_y, node_idx=node_indice)
```

Next, we use the SubgraphX method from our DIG library to explain this prediction. In SubgraphX, the target node and its 2-hop neighbors are considered as the computation graph. Then it uses the Monte Carlo Tree Search to explore different subgraphs. For each subgraph, the Shapley value is approximated to measure the importance.

```
from dig.xgraph.method import SubgraphX
explainer = SubgraphX(model, num_classes=4, device=device, explain_graph=False,
reward_method='nc_mc_l_shapley')
```

For more details of our DIG implementations, please refer to

After MCTS searching and Shapley value computation, the subgraph with the highest importance score is selected as the explanation. Note that we can apply an upper bound on the size of the explanation so that the explanation succinct enough. Then we can show the visualization of the explanation results:

```
from dig.xgraph.method.subgraphx import find_closest_node_result
plotutils = PlotUtils(dataset_name='ba_shapes')
# Visualization
max_nodes = 5
node_idx = node_indices[20]
print(f'explain graph node {node_idx}')
data.to(device)
logits = model(data.x, data.edge_index)
prediction = logits[node_idx].argmax(-1).item()
_, explanation_results, related_preds = \
explainer(data.x, data.edge_index, node_idx=node_idx, max_nodes=max_nodes)
result = find_closest_node_result(explanation_results[prediction], max_nodes=max_nodes)
plotutils = PlotUtils(dataset_name='ba_shapes')
explainer.visualization(explanation_results,
prediction,
max_nodes=max_nodes,
plot_utils=plotutils,
y=data.y)
```

Obviously, the SubgraphX successfully and precisely identifies the house-like motif as the important subgraph for the target blue node’s prediction. It is consistent with the rule we build the dataset.

Furthermore, we can quantitatively evaluate the explanation. Specifically, we use the fidelity metric and sparsity metric, which are supported by our DIG library. The fidelity metric measures the difference between predictions by occluding the important subgraph from the original graph while the sparsity metric measures the size of the explanation. Here we show the fidelity and sparsity of the above explanation.

```
max_nodes = 5
node_idx = node_indices[20]
_, explanation_results, related_preds = \
explainer(data.x, data.edge_index, node_idx=node_idx, max_nodes=max_nodes)
result = find_closest_node_result(explanation_results[prediction], max_nodes=max_nodes)
related_preds[prediction]
```

Results: fidelity: 0.1384, sparsity: 0.6429

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