TY - GEN
T1 - Adversarial Mask Explainer for Graph Neural Networks
AU - Zhang, Wei
AU - Li, Xiaofan
AU - Nejdl, Wolfgang
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/5/13
Y1 - 2024/5/13
N2 - The Graph Neural Networks (GNNs) model is a powerful tool for integrating node information with graph topology to learn representations and make predictions. However, the complex graph structure of GNNs has led to a lack of clear explainability in the decision-making process. Recently, there has been a growing interest in seeking instance-level explanations of the GNNs model, which aims to uncover the decision-making process of the GNNs model and provide insights into how it arrives at its final output. Previous works have focused on finding a set of weights (masks) for edges/nodes/node features to determine their importance. These works have adopted a regularization term and a hyperparameter K to control the explanation size during the training process and keep only the top-K weights as the explanation set. However, the true size of the explanation is typically unknown to users, making it difficult to provide reasonable values for the regularization term and K. In this work, we propose a novel framework AMExplainer which leverages the concept of adversarial networks to achieve a dual optimization objective in the target function. This approach ensures both accurate prediction of the mask and sparsity of the explanation set. In addition, we devise a novel scaling function to automatically sense and amplify the weights of the informative part of the graph, which filters out insignificant edges/nodes/node features for expediting the convergence of the solution during training. Our extensive experiments show that AMExplainer yields a more compelling explanation by generating a sparse set of masks while simultaneously maintaining fidelity.
AB - The Graph Neural Networks (GNNs) model is a powerful tool for integrating node information with graph topology to learn representations and make predictions. However, the complex graph structure of GNNs has led to a lack of clear explainability in the decision-making process. Recently, there has been a growing interest in seeking instance-level explanations of the GNNs model, which aims to uncover the decision-making process of the GNNs model and provide insights into how it arrives at its final output. Previous works have focused on finding a set of weights (masks) for edges/nodes/node features to determine their importance. These works have adopted a regularization term and a hyperparameter K to control the explanation size during the training process and keep only the top-K weights as the explanation set. However, the true size of the explanation is typically unknown to users, making it difficult to provide reasonable values for the regularization term and K. In this work, we propose a novel framework AMExplainer which leverages the concept of adversarial networks to achieve a dual optimization objective in the target function. This approach ensures both accurate prediction of the mask and sparsity of the explanation set. In addition, we devise a novel scaling function to automatically sense and amplify the weights of the informative part of the graph, which filters out insignificant edges/nodes/node features for expediting the convergence of the solution during training. Our extensive experiments show that AMExplainer yields a more compelling explanation by generating a sparse set of masks while simultaneously maintaining fidelity.
KW - explainability
KW - graph analysis
KW - graph neural networks
UR - http://www.scopus.com/inward/record.url?scp=85194067054&partnerID=8YFLogxK
U2 - 10.1145/3589334.3645608
DO - 10.1145/3589334.3645608
M3 - Conference contribution
AN - SCOPUS:85194067054
SP - 861
EP - 869
BT - WWW `24
T2 - 33rd ACM Web Conference, WWW 2024
Y2 - 13 May 2024 through 17 May 2024
ER -