Propagation Identity Perturbations: A New Adversarial Threat Model for Robust Social Event Forecasting

Social event forecasting models depend critically on who participates in the propagation process, which differs from conventional graph learning tasks. Existing robustness research in this area intensively studies perturbations on graph topology, node features, or textual content, but leaves propagation identities underexplored. We propose Propagation Identity Perturbation (PIP), a new adversarial threat model that formalizes attacks on participant identities in temporal diffusion trajectories while preserving realistic propagation behavior. We instantiate PIP as a gradient-guided discrete attack that searches behaviorally plausible identity substitutions within constrained candidate spaces. Moreover, we provide theoretical support for PIP in feasible search space reduction, transferability lower bound, monotonic ascent property, and guaranteed loss disruption bound. Extensive experiments on five representative social event forecasting architectures against five mainstream defense measures further expose the severity of this overlooked vulnerability in diffusion forecasting systems. These findings highlight an urgent need to develop identity-aware defense mechanisms to defend social event forecasting models against PIP.