Efficient influence spread management via budget allocation at community scale

Abstract Given a social network, the classical influence maximization (IM) and misinformation prevention (MP) problems both adopt similar seed triggering models, i.e., convincing k specific users to become seed nodes by material incentive (e.g. free products). However, in real life, those chosen seeds may not be willing to spread information as expected, which will affect the final diffusion. Instead of convincing one single user, we can target a user community, in the hope that some of them may turn into propagation seeds voluntarily. This community-based seed (Com-seed) triggering model can be used in real-world applications such as distributing flyers or offering discounts in local communities, where the objective is to maximize the promotion effect with the given budget constraints. In this paper, we aim to maximize the influence or minimize the misinformation spread by finding an optimal community-based budget allocation under the Com-seed triggering models. We present new formulations of the influence maximization and misinformation prevention problems from the community perspective and design effective and scalable algorithms to solve these new problems. With intricately designed community-based sampling schemes and approximation guarantee of the greedy approach over integer lattice, our algorithms can achieve ( 1 - 1 / e - ∊ ) -approximation results. Experiments show our methods outperform all baselines and run faster than the state-of-the-art methods in both influence maximization and misinformation prevention problems, which demonstrate the effectiveness and scalability of the proposed algorithms.