  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.
