A hierarchy based influence maximization algorithm in social networks

Influence maximization refers to mining top-K most influential nodes from a social network to maximize the final propagation of influence in the network, which is one of the key issues in social network analysis. It is a discrete optimization problem and is also NP-hard under both independent cascade and linear threshold models. The existing researches show that although the greedy algorithm can achieve an approximate ratio of (1-1/e), its time cost is expensive. Heuristic algorithms can improve the efficiency, but they sacrifice a certain degree of accuracy. In order to improve efficiency without sacrificing much accuracy, in this paper, we propose a new approach called Hierarchy based Influence Maximization algorithm (HBIM in short) to mine top-K influential nodes. It is a two-phase method: (1) an algorithm for detecting information diffusion levels based on the first-order and second-order proximity between social nodes. (2) a dynamic programming algorithm for selecting levels to find influential nodes. Experiments show that our algorithm outperforms the benchmarks.