Stop and Stare: Optimal Sampling Algorithms for Viral Marketing in Billion scale Networks
Influence Maximization (IM) that seeks a small set of key users who spread the influence widely into the network, is a core problem in multiple domains. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. Despite the huge amount of effort, IM in billion scale networks such as Facebook, Twitter, and World Wide Web has not been satisfactorily solved. Even the state of the art methods such as TIM+ and IMM may take days on those networks. In this paper, we propose SSA and D SSA, two novel sampling frameworks for IM based viral marketing problems. SSA and D SSA are up to 1200 times faster than the SIGMOD'15 best method, IMM, while providing the same (1 1/e e) approximation guarantee. Underlying our frameworks is an innovative Stop and Stare strategy in which they stop at exponential check points to verify (stare) if there is adequate statistical evidence on the solution quality. Theoretically, we prove that SSA and D SSA are the first approximation algorithms that use (asymptotically) minimum numbers of samples, meeting strict theoretical thresholds characterized for IM. The absolute superiority of SSA and D SSA are confirmed through extensive experiments on real network data for IM and another topic aware viral marketing problem, named TVM.
SIGMOD Conference