Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks

TL;DR

This paper introduces SSA and D-SSA, two fast and theoretically sound sampling algorithms for influence maximization in billion-scale networks, significantly outperforming previous methods in speed while maintaining approximation guarantees.

Contribution

The paper presents novel Stop-and-Stare sampling frameworks, SSA and D-SSA, that achieve near-optimal sample complexity and drastically improve efficiency for influence maximization in large networks.

Findings

SSA and D-SSA are up to 1200 times faster than IMM.

They maintain the same approximation guarantees as previous state-of-the-art methods.

Extensive experiments confirm their superior performance on real-world networks.

Abstract

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 $(1-1/e-\epsilon)$ 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. The source code is available at https://github.com/hungnt55/Stop-and-Stare