Residual Entropy-based Graph Generative Algorithms

Classification and clustering are crucial tasks that recognize the identities and the communities of nodes in a graph. Several methods have been proposed to reduce the accuracy of node classification and clustering through graph neural networks (GNN). Existing defense methods usually modify the model architecture and adopt countermeasure training to enhance the robustness of the node classification and clustering. However, these defense methods are model-oriented and not robust. To alleviate the problem, this paper first proposes a robust node classification metric based on residual entropy. More concretely, we prove that maximizing the residual entropy helps to improve the robustness of the classification accuracy. We them propose two graph generative algorithms to resist against two kinds of GNN-based attacks, the untargeted and the targeted attacks. Finally, experimental analysis show that the proposed algorithms outperform the existing defense works under five classic datasets.