Scalable Influence Maximization with General Marketing Strategies

In this paper, we study scalable algorithms for influence maximization with general marketing strategies (IM-GMS), in which a marketing strategy mix is modeled as a vector x = (x1, . . . , xd) and could activate a node v in the social network with probability hv(x). The IM-GMS problem is to find the best strategy mix x∗ that maximize the influence spread due to influence propagation from the activated seeds, subject to the budget constraint that ∑ j∈[d] xj ≤ k. We adapt the scalable reverse influence sampling (RIS) approach and design a scalable algorithm that provides a (1−1/e−ε) approximate solution (for any ε > 0), with running time near-linear in the network size. We further extend IM-GMS to allow partitioned budget constraint, and show that our scalable algorithm provides a (1/2 − ε) solution in this case. Through extensive experiments, we demonstrate that our algorithm is several orders faster than the Monte Carlo simulation based hill-climbing algorithm, and also outperforms other baseline algorithms proposed in the literature.