Graph Neural Network （GNN） is vulnerable to adversarial attacks， leading to performance degradation， which affects downstream tasks such as node classification， link prediction and community detection. Therefore， the defense methods of GNN have important research value. Aiming at the problem that GNN has poor robustness when being adversarially attacked， taking Graph Convolutional Network （GCN） as the model， an improved Singular Value Decomposition （SVD） based poisoning attack defense method was proposed， named ISVDatt. In the poisoning attack scenario， the attacked graph was able to be purified by the proposed method. When the GCN was attacked by poisoning， the connected edges with large different features were first screened and deleted to keep the graph features smooth. Then， SVD and low-rank approximation operations were performed to keep the low rank of the attacked graph and clean it up. Finally， the purified graph was used for training GCN model to achieve effective defense against poisoning attack. Experiments against Metattack and DICE were conducted on the open source datasets such as Citeseer， Cora and Pubmed， and compared with the defense methods based on SVD， Pro_GNN and Robust Graph Convolutional Network （RGCN）， respectively. The results show that ISVDatt has relatively better defense effect， although the classification accuracy is lower than that of Pro_GNN， but it has low complexity and negligible time overhead. Experimental results verify that ISVDatt can resist poisoning attack effectively with the consideration of both the complexity and versatility of the algorithm， and has a high practical value.
