Containing Viral Spread on Sparse Random Graphs: Bounds, Algorithms, and Experiments

Abstract

Viral spread on large graphs has many real-life applications such as malware propagation in computer networks and rumor (or misinformation) spread in Twitter-like online social networks. Although viral spread on large graphs has been intensively analyzed on classical models such as Susceptible–Infectious–Recovered, there still exits a deficit of effective methods in practice to contain epidemic spread once it passes a critical threshold. Against this backdrop, we explore methods of containing viral spread in large networks with the focus on sparse random networks. The viral containment strategy is to partition a large network into small components and then to ensure that all messages delivered across different components are free of infection. With such a defense mechanism in place, an epidemic spread starting from any node is limited to only those nodes belonging to the same component as the initial infection node. We establish both lower and upper bounds on the costs of inspecting intercomponent messages. We further propose heuristic-based approaches to partitioning large input graphs into small components. Finally, we study the performance of our proposed algorithms under different network topologies and different edge-weight models.