Residual Entropy-based Graph Generative Algorithms

Abstract

**Read paper on the following link:** https://ifaamas.org/Proceedings/aamas2022/pdfs/p816.pdf **Abstract:** Classification and clustering are prevalent to recognize the identity and the community of nodes in graphs. Several studies analyzed graph structure to build graph neural network (GNN) models for reducing the accuracy of node classification and clustering recently. The existing defense usually modified the structure of models and adopted countermeasure training to enhance the robustness of the node classification and clustering. However, these defense works are model-oriented and not robust. To alleviate the problem, this paper first proposed a robust node classification metric based on residual entropy. More concrete, the accuracy of node classification is proven robustly by maximizing the residual entropy of the nodes theoretically. Then two graph generative algorithms are proposed to resist against two kinds of GNN-based attacks, the untargeted attack and the targeted attack, respectively. Finally, experimental analysis show that the proposed algorithms outperform the existing defense works under five classic datasets.