Blocking Self-avoiding Walks Stops Cyber-epidemics: A Scalable GPU-based Approach

Cyber-epidemics, thewidespread of fake news or propaganda through social media, can cause devastating economic and political consequences. A common countermeasure against cyber-epidemics is to disable a small subset of suspected social connections or accounts to e‚ectively contain the epidemics. An example is the recent shutdown of 125,000 ISIS-related TwiŠer accounts. Despite many proposed methods to identify such subset, none are scalable enough to provide high-quality solutions in nowadays billion-size networks. To this end, we investigate the Spread Interdiction problems that seek most e‚ective links (or nodes) for removal under the well-known Linear Œreshold model. We propose novel CPU-GPU methods that scale to networks with billions of edges, yet, possess rigorous theoretical guarantee on the solution quality. At the core of our methods is an O(1)-space out-of-core algorithm to generate a new type of random walks, called Hiˆing Self-avoiding Walks (HSAWs). Such a low memory requirement enables handling of big networks and, more importantly, hiding latency via scheduling of millions of threads on GPUs. Comprehensive experiments on realworld networks show that our algorithms provides much higher quality solutions and are several order of magnitude faster than the state-of-the art. Comparing to the (single-core) CPU counterpart, our GPU implementations achieve signi€cant speedup factors up to 177x on a single GPU and 338x on a GPU pair.