Overview of Explainability in GNN
GNNs (Graph Neural Networks) are neural networks for handling graph-structured data, which use node and edge (vertex and edge) information to capture patterns and structures in graph data, and are applicable to social network analysis, chemical structure prediction, recommendation systems, and graph-based anomaly detection. This method is applied to social network analysis, chemical structure prediction, recommendation systems, graph-based anomaly detection, etc.
While the neural networks used in GNNs have enabled advanced machine learning, it has become increasingly difficult for humans to interpret the reasons for their behavior, and explainability (Explainable AI, XAI) technology has attracted attention in recent years as an important issue. This is no exception in GNNs.
Explainability in GNNs is important from the following perspectives
- Increased reliability: Understanding what the model is learning improves the reliability of the model. Especially in areas such as healthcare and finance, it is necessary to understand the decision-making process of the model.
- Improving the model: Through explainability, weaknesses and mistakes in the model can be identified and improved. Knowing why inaccurate forecasts were made can provide clues for improving the accuracy of the model.
- Gaining user trust: End-users and stakeholders can build trust in the model by understanding the model’s results and decision-making process.
GNN explainability methods include the following
- Feature Importance: Visualizing the importance of a node or edge feature allows users to understand the extent to which each element contributes to the prediction. This allows, for example, to show the impact of a particular node on the forecast.
- Visualization of graph regions of interest: It is possible to visualize which parts of the graph the model focuses on when making a particular prediction. This allows one to understand what patterns and structures the model is focusing on.
- Extracting important paths in a graph: Identifying important paths or subgraphs in a graph allows one to understand the factors that influence predictions.
Local interpretation: by focusing on specific nodes or edges, it is possible to explain how they influence the forecast.
One specific tool is GNNExplainer, reported by Ying in “GNNExplainer: Generating Explanations for Graph Neural Networks“. It describes how features of surrounding nodes affect classification and prediction when aggregating features for a node of interest in learning representations of graph neural networks, and is useful for node classification, graph classification, and link prediction in graph neural networks such as GCN, GraphSAGE described in “GraphSAGE Overview, Algorithm, and Example Implementation“, GAT, and SGC. It can be used for node classification, graph classification, and link prediction in graph neural networks such as GCN, GraphSAGE, GAT, and SGC.
Grad-CAM, reported by Pope et al. in “Explainability Methods for Graph Convolutional Neural Networks,” also explains the behavior of graph convolutional networks by changing the color of the predictions that were affected. The Grad-CAM also explains the behavior of graph convolutions by changing the color of the predictive affected parts.
For an overview of research on explainability in graph neural networks, see “Explainability in Graph Neural Networks: A Taxonomic Survey” by Tuan et al. In this paper, the explanatory techniques are categorized as follows: type of explanation (instance-level or model-level), training (whether the explanation involves a learning process or not), task (node or graph classification), object of explanation (node, edge, or node feature), whether the GNN is treated as a black box Whether or not the GNN is treated as a black box, the computational flow of the explanation (forward or backward), and the design (whether the explanation is for graphs or for images) are considered.
Algorithms related to explainability in GNN
Various algorithms and methods have been proposed to improve explainability in Graph Neural Networks (GNNs). Some of them are introduced below. 1.
1. GNNExplainer: GNNExplainer is a method for explaining the results of predictions and inferences made by GNNs. The method identifies important nodes and edges for a given graph and target node and visualizes their importance The basic idea of GNNExplainer is as follows.
- Randomly mask nodes and edges to modify the graph and examine how the changes affect the prediction.
- To identify important nodes and edges, optimize them in such a way that the change in prediction before and after the change is minimized.
- This method allows us to visualize the important areas that contribute to the forecast and explain the decision-making process of the model.
2. integrated gradients for GNNs (IGNNs): Integrated Gradients is a general method for interpreting the factors that contribute to the model’s forecast, but in an extended version for application to GNNs The basic idea of IGNNs is to be.
- Compute the importance of a particular node or edge for an input graph to obtain model predictions.
- This calculation is done by modifying the graph so that it does not include the target node or edge, while observing how the predictions change.
- This process is integrated to compute the total importance of the nodes and edges.
- This method quantifies the degree to which each element has an impact on the forecast and displays it in an interpretable format.
3. GraphLIME: GraphLIME is a method for providing local explanatory possibilities. The basic idea of GraphLIME is as follows: for a given target node, extract its surrounding neighborhood subgraphs and explain how they affect the model’s predictions.
- Randomly sample the neighborhood subgraphs of the target node and examine the model’s predictions for these subgraphs.
- Calculate the difference between the prediction of each subgraph and the prediction of the original graph and evaluate its importance.
- In this way, local graph patterns that contribute to the prediction of the target node are extracted and displayed in an explicable form.
Application of Explainability in GNN
Specific applications of explainability in GNNs are described below.
1. social network analysis: GNNs have been widely applied to social network analysis, and the following are some examples from the viewpoint of explainability.
Community identification: When analyzing social networks using GNNs, it is possible to explain the reasons and characteristics that lead to the formation of specific communities or clusters. This allows us to understand how groups form and what they share.
Information diffusion analysis: GNNs can be used to explain patterns of information diffusion, making it possible to identify how a particular node spreads information and how it is affected.
2. chemical structure prediction: In chemistry, GNNs are used to predict the properties of molecules and the activity of compounds. Examples of applications of explanatory possibilities include the following
Interpretation of molecular features: When using GNNs to predict molecular properties, it is possible to explain which atoms and bonds influence the prediction. This can be used to help design new drugs and improve compounds.
Understand chemical reactions: When modeling chemical reactions using GNNs, explain the mechanism of the reaction and the active site. This can provide insight to improve reaction rates and selectivity.
3. recommendation systems: GNNs have also been widely applied to recommendation systems, where the characteristics of individual users and items are taken into account in making recommendations. Examples of the application of explainability include the following
Explanation of the basis of recommendation: GNNs can explain the basis of recommendations made by GNNs, such as the behavior of a particular user or the characteristics of an item, making it possible, for example, to interpret why a particular product was recommended to a particular user.
Detecting bias: When using GNNs to make recommendations, bias or unfairness toward a particular user or item can be detected. This allows for measures to be taken to ensure the fairness of the system.
4. graph-based anomaly detection: GNNs are also used for anomaly detection to detect fraud and system anomalies. Examples of the application of explainability include the following
Interpretation of anomalous patterns: When a GNN detects an anomaly, it can explain the anomalous pattern or characteristics of the node. This makes it possible to understand why the node or pattern was detected as an anomaly.
Elucidating the causes of anomalies: By explaining the causes or factors that led to the GNN detecting an anomaly, the safety and security of the system can be improved. For example, anomalies in financial transactions can be explained to help detect fraud.
Examples of Explainability Implementations in GNNs
There are several ways to implement explainability in GNNs (Graph Neural Networks, or graph neural networks). Examples of implementations using Python and PyTorch are described below.
1. Implementation of GNNExplainer: GNNExplainer is a method for explaining predictions of GNNs. The following is an example implementation of GNNExplainer using PyTorch Geometric
import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.datasets import Planetoid
from torch_geometric.utils import k_hop_subgraph
from torch_geometric.nn import GNNExplainer
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = GCNConv(dataset.num_features, 16)
self.conv2 = GCNConv(16, dataset.num_classes)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = self.conv1(x, edge_index)
x = F.relu(x)
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
dataset = Planetoid(root='data/Planetoid', name='Cora')
data = dataset[0]
model = Net()
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
explainer = GNNExplainer(model)
node_idx = 0 # Specify the index of the node you want to describe here
node_feat_mask, edge_mask = explainer.explain_node(node_idx, data)
print("Node Feature Mask:", node_feat_mask)
print("Edge Mask:", edge_mask)In this example, Cora from the Planetoid dataset is used to define a Graph Convolutional Network (GCN) and GNNExplainer is used to obtain a description of a particular node.
2. implementation of IGNNs (Integrated Gradients for GNNs): Integrated Gradients become a method for interpreting the elements that contribute to the model’s predictions; IGNNs are an adapted version of GNNs; the following is a PyTorch Geometric The following is an example of implementing IGNNs using PyTorch Geometric.
import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.datasets import Planetoid
from captum.attr import IntegratedGradients
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = GCNConv(dataset.num_features, 16)
self.conv2 = GCNConv(16, dataset.num_classes)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = self.conv1(x, edge_index)
x = F.relu(x)
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
dataset = Planetoid(root='data/Planetoid', name='Cora')
data = dataset[0]
model = Net()
model.eval()
data = dataset[0]
x, edge_index = data.x, data.edge_index
ig = IntegratedGradients(model)
node_idx = 0 # Specify the index of the node you want to describe here
attribution = ig.attribute(x.unsqueeze(0), additional_forward_args=(edge_index,), target=node_idx)
print("Node Attribution:", attribution)In this example, the GCN is defined and IntegratedGradients is used to obtain a description of a particular node.
Challenges of Accountability in GNN and Measures to Address Them
Below we discuss the challenges of explainability in GNNs and how to deal with them.
1. Interpreting Complex Graph Structures:
Challenge: Because GNNs deal with complex graph structures, it is difficult to interpret the decision-making process of the model, especially to clearly understand which nodes and edges are contributing to the prediction.
Solution:
Local interpretation: It is useful to focus on specific nodes or edges when the model makes a particular prediction, so that we can understand the impact of specific local patterns or structures on the prediction.
Visualization of regions of interest in the graph: visualization of the areas of interest when the GNN makes predictions can help understand what patterns and structures the model is focusing on.
Extracting important pathways in the graph: identifying important pathways and sub-graphs and interpreting how these influence the forecast.
2. Interpreting the interaction of multiple features:
Challenge: GNNs consider multiple nodes and edge features and make predictions through their interactions. This makes it difficult to interpret how feature interactions affect predictions.
Solution:
Calculate the importance of the features: the importance of the features at each node or edge can be calculated to visualize how much they contribute to the prediction.
Integrated Gradients: a method for interpreting the factors that contribute to the model’s predictions, which helps quantify the relative importance of features.
3. Effects of Graph Size:
Challenge: As graph size increases, interpreting model predictions becomes more difficult, especially as computational costs increase and interpretability accuracy decreases.
Solution:
Sampling and reduction: For large graphs, sampling and reduction can be performed to streamline interpretability calculations.
Partitioning and Integration: A large graph can be interpreted by partitioning the graph into multiple subgraphs, interpreting each one separately, and then integrating them.
4. Understanding the importance of nodes:
Challenge: It is important to understand the important nodes that contribute to a particular forecast, but it can be difficult to understand which nodes are important.
Solution:
Use GNNExplainer: GNNExplainer calculates and visualizes the importance of specific nodes and edges to help understand what factors contribute to that forecast.
Local interpretation: Focusing on specific nodes and explaining how they influence the forecast can be useful.
Reference Information and Reference Books
For more information on graph data, see “Graph Data Processing Algorithms and Applications to Machine Learning/Artificial Intelligence Tasks. Also see “Knowledge Information Processing Techniques” for details specific to knowledge graphs. For more information on deep learning in general, see “About Deep Learning.
Reference book is
“Graph Neural Networks: Foundations, Frontiers, and Applications“等がある。
“Introduction to Graph Neural Networks“
“Graph Neural Networks in Action“
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