Structure-Aware Optimal Intervention for Rumor Dynamics on Networks: Node-Level, Time-Varying, and Resource-Constrained

TL;DR

This paper introduces a dynamic, resource-efficient intervention framework for controlling rumor spread in social networks, optimizing node-level actions over time based on network structure and diffusion state.

Contribution

It presents a novel, coupled optimal control approach that adapts resource allocation over time, outperforming static heuristics in reducing rumor spread.

Findings

Reduces peak and total infections compared to baseline methods.

Adapts resource focus from influential hubs to peripheral nodes over time.

Provides a scalable, interpretable strategy for misinformation control.

Abstract

Rumor propagation in social networks undermines social stability and public trust, calling for interventions that are both effective and resource-efficient. We develop a node-level, time-varying optimal intervention framework that allocates limited resources according to the evolving diffusion state. Unlike static, centrality-based heuristics, our approach derives control weights by solving a resource-constrained optimal control problem tightly coupled to the network structure. Across synthetic and real-world networks, the method consistently lowers both the infection peak and the cumulative infection area relative to uniform and centrality-based static allocations. Moreover, it reveals a stage-aware law: early resources prioritize influential hubs to curb rapid spread, whereas later resources shift to peripheral nodes to eliminate residual transmission. By integrating global efficiency with fine-grained adaptability, the framework offers a scalable and interpretable paradigm for misinformation management and crisis response.