Evolving Influence Maximization

Influence Maximization (IM) aims to maximize the number of people that become aware of a product by finding the ‘best’ set of ‘seed’ users to initiate the product advertisement. Unlike prior arts on static social networks containing fixed number of users, we undertake the first study of IM in more realistic evolving networks with temporally growing topology. The task of evolving IM (EIM), however, is far more challenging over static cases in the sense that seed selection should consider its impact on future users and the probabilities that users influence one another also evolve over time. We address the challenges through EIM, a newly proposed bandit-based framework that alternates between seed nodes selection and knowledge (i.e., nodes’ growing speed and evolving influences) learning during network evolution. Remarkably, EIM involves three novel components to handle the uncertainties brought by evolution: (1) A fully adaptive particle learning of nodes’ growing speed for accurately estimating future influenced size, with real growing behaviors delineated by a set of weighted particles. (2) A bandit-based refining method with growing arms to cope with the evolving influences via growing edges from previous influence diffusion feedbacks. (3) Evo-IMM, a priority based seed selection algorithm with the objective to maximize the influence spread to highly attractive users during evolution. Theoretically, EIM returns a regret bound that provably maintains its sublinearity with respect to the growing network size. Empirically, the effectiveness of EIM are also validated, with three notable million-scale evolving network datasets possessing complete social relationships and nodes’ joining time. The results confirm the superiority of EIM in terms of an up to 50% larger influenced size over four static baselines.