ASIM: A Scalable Algorithm for Influence Maximization under the Independent Cascade Model

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. Although, TIM is one of the fastest existing algorithms, it cannot be deemed scalable owing to its exorbitantly high memory footprint.cIn this paper, we address the scalability aspect -- memory consumption and running time of the influence maximization problem. We propose ASIM, a scalable algorithm capable of running within practical compute times on commodity hardware. Empirically, ASIM is $6-8$ times faster when compared to CELF++ with similar memory consumption, while its memory footprint is $\approx 200$ times smaller when compared to TIM.