Hierarchy-based algorithm for the influence maximization problem in social networks

In this paper we build a framework of up-to-2-hop hierarchical influence to approximate the spreading of social influence, and further propose a hierarchy-based algorithm to solve the influence maximization problem in social networks. Based on the heuristic of up-to-2-hop influence estimation, the influential nodes are selected according to the marginal influence increment. Our proposed framework and algorithm are applicable to the two widely used diffusion models, namely, the Linear Threshold Model and the Independent Cascade Model. We utilize a collaboration network and a who-trust-whom online social network to test our algorithm, and compare it with several existing heuristics, namely, the pure greedy algorithm, the centrality-based scheme, the single discount and the degree discount heuristics. We find that our proposed algorithm performs better than the degree-based scheme, the single discount and the degree discount heuristics, while achieving approximately the same performance as the greedy algorithm. The computational load is dramatically lower than the greedy heuristic.