Influence Maximization in Near-Linear Time

Abstract

Given a social network G and a positive integer k, the influence maximization problem asks for k nodes (in G) whose adoptions of a certain idea or product can trigger the largest expected number of follow-up adoptions by the remaining nodes. This problem has been extensively studied in the literature, and the state-of-the-art technique runs in O((k+l) (n+m) log n ε2) expected time and returns a (1-1 e-ε)-approximate solution with at least 1 - 1/n l probability.