A2S2-GNN: Rigging GNN-Based Social Status by Adversarial Attacks in Signed Social Networks

Social status, the social influence of a user, plays an important role in many real-world applications, e.g., trust relations and information propagation in a social network. In this paper, we reveal the possibility of falsifying social status through adversarial attacks in graph neural networks (GNNs). Different from neural networks in the visual or speech domain, GNNs take the attributes of nodes and edges in a graph as features. To cater to the characteristics of GNNs,<inline-formula> <tex-math notation="LaTeX">$\vphantom {_{\int }}$ </tex-math></inline-formula> we design a new paradigm of adversarial example attack, named <inline-formula> <tex-math notation="LaTeX">$A^{2} S^{2}$ </tex-math></inline-formula>- GNN (<inline-formula> <tex-math notation="LaTeX">$\mathbf {GNN}$ </tex-math></inline-formula>-based <inline-formula> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula>dversarial <inline-formula> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula>ttacks on <inline-formula> <tex-math notation="LaTeX">$\mathbf {S}$ </tex-math></inline-formula>ocial <inline-formula> <tex-math notation="LaTeX">$\mathbf {S}$ </tex-math></inline-formula>tatus), aiming at manipulating the social status of a target node in social networks. The key idea is to establish relationships or break relationships between a set of compromised nodes and the target node. More specifically, we consider a signed directed graph representing complicated positive/negative asymmetric relationships between nodes. We design an efficient adversarial attack algorithm to determine the minimum set of signed links that should be created or deleted to reach the attack objective. We conduct extensive experiments on baseline datasets. Compared with the benchmark algorithms, <inline-formula> <tex-math notation="LaTeX">$A^{2} S^{2}$ </tex-math></inline-formula>- GNN can effectively promote or vilify the social status of the target node up to 89.36% and 192.38%, respectively, while keeping the modification to the social network to the minimum. Furthermore, the experimental results on six status evaluating algorithms verify the transferability of our proposed attack algorithm.