Node Immunization over Infectious Period

Chonggang Song, Wynne Hsu, Mong-Li Lee

Abstract: Locating nodes to immunize in computer/social networks to control the spread of virus or rumors has become an important problem. In real world contagions, nodes may get infected by external sources when the propagation is underway. While most studies formalize the problem in a setting where contagion starts at one time point, we model a more realistic situation where there are likely to be many breakouts of contagions over a time window. We call this the node immunization over infectious period (NIIP) problem. We show that the NIIP problem is NP-hard and remains so even in directed acyclic graphs. We propose a NIIP algorithm to select $k$ nodes to immunize over a time period. Simulation is performed to estimate a good distribution of $k$ over the time period. For each time point, the NIIP algorithm will make decisions which nodes to immunize given the estimated value of $k$ for that time point. Experiments show that the proposed NIIP algorithm outperform the state-of-the-art algorithms in terms of both effectiveness and efficiency.