An efficient and effective hop-based approach for influence maximization in social networks

Influence maximization in social networks is a classic and extensively studied problem that targets at selecting a set of initial seed nodes to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale social networks. Prior Monte Carlo simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can be easily applied to billion-scale networks under the commonly used Independent Cascade and Linear Threshold influence diffusion models. Moreover, we provide provable data-dependent approximation guarantees for our proposed hop-based approaches. Experimental evaluations with real social network datasets demonstrate the efficiency and effectiveness of our algorithms.