The problem of online social network manipulation for community canvassing is of real concern in today's world. Motivated by the study of voter models, opinion and polarization dynamics on networks, we model community canvassing as a dynamic process over a network enabled via gradient-based attacks on GNNs. Existing attacks on GNNs are all single-step and do not account for the dynamic cascading nature of information diffusion in networks. We consider the realistic scenario where an adversary uses a GNN as a proxy to predict and manipulate voter preferences, especially uncertain voters. Gradient-based attacks on the GNN inform the adversary of strategic manipulations that can be made to proselytize targeted voters. In particular, we explore $\textit{minimum budget attacks for community canvassing}$ (MBACC). We show that the MBACC problem is NP-Hard and propose Dynamic Multi-Step Adversarial Community Canvassing (MAC) to address it. MAC makes dynamic local decisions based on the heuristic of low budget and high second-order influence to convert and perturb target voters. MAC is a dynamic multi-step attack that discovers low-budget and high-influence targets from which efficient cascading attacks can happen. We evaluate MAC against single-step baselines on the MBACC problem with multiple underlying networks and GNN models. Our experiments show the superiority of MAC which is able to discover efficient multi-hop attacks for adversarial community canvassing. Our code implementation and data is available at https://github.com/saurabhsharma1993/mac.

翻译：在线社交网络操纵以进行社区拉票的问题在当今世界备受关注。受选民模型、网络意见与极化动力学研究的启发，我们将社区拉票建模为通过梯度攻击图神经网络（GNN）实现的网络动态过程。现有对GNN的攻击均为单步攻击，未考虑信息在网络中扩散的动态级联特性。我们考虑了一个现实场景：攻击者使用GNN作为代理来预测和操纵选民偏好，尤其是摇摆选民。对GNN的梯度攻击告知攻击者可通过策略性操作来改变特定选民立场。特别地，我们研究了**社区拉票的最小预算攻击问题**（MBACC）。我们证明MBACC问题是NP难的，并提出动态多步对抗社区拉票方法（MAC）来解决该问题。MAC基于低预算与高二阶影响力的启发式规则进行动态局部决策，以转化和扰动目标选民。MAC是一种动态多步攻击方法，可发现低预算、高影响力的目标节点，从而触发高效级联攻击。我们在多个底层网络和GNN模型上，将MAC与单步基线方法在MBACC问题中进行了评估。实验表明MAC具有优越性，能够发现用于对抗性社区拉票的高效多跳攻击。我们的代码实现和数据可在https://github.com/saurabhsharma1993/mac获取。