Adversarial Attack Algorithm for Introducing Degree Centrality Selection of Attack Nodes

Graph convolutional networks (GCN) are widely used in graph neural networks and play an important role in processing graph structured data. However, recent studies have shown that GCN is vulnerable to malicious attacks such as poisoning attacks. Of all the possible adversarial attacks against GCN, one particular approach is the TUA(target universal attack) against graph convolutional networks. In order to select attack nodes simply, the method adopts random selection strategy, which ignores the importance of neighbor to node, and has a negative impact on the success rate of attack. In response to this problem, adversarial attack algorithm based on degree centrality attack node selection strategy, adversarial attack algorithm based on degree centrality attack node selection strategy (DCANSS). Firstly, the method of selecting attack nodes is optimized, and the degree centrality is introduced to get the attack nodes. Secondly, the fake node is injected and connected to the attack node. Then, the auxiliary node is selected and the message passing mechanism of the graph convolutional network is applied to spread the node information, calculate the disturbance and assign the disturbance feature to the false node to complete the attack and achieve the misclassification goal. Experiments on three popular datasets show that when only three attack nodes and six fake nodes are used, the proposed attack has an average success rate of 90% against any victim node in the graph. By comparing DCANSS algorithm with TUA algorithm and other established baseline algorithms, the attack capability of DCANSS algorithm is further verified. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.