Influence Maximization with Reverse Influence Sampling and Evolutionary Algorithm

Identifying influential nodes in social networks is an important problem called the influence maximization (IM) problem. So far, a large number of IM algorithms have been proposed. Among these algorithms, meta-heuristic approaches such as evolutionary algorithms (EAs) can obtain high-quality solutions. But in general, they usually suffer from time efficiency problems and are designed only for a few diffusion models. In this paper, we propose a novel EA combined with the reverse influence sampling (RIS) to solve the IM problem. By introducing the RIS technique, we can evaluate influence spreading efficiently under various diffusion models using hypergraphs. Moreover, the hypergraphs are also used as a kind of high-level heuristic information. To combine the RIS technique with EA, we exploit the idea of RIS to design a surrogate model and decide to address the single-objective IM problem in a multi-objective way. Then we modify the classical NSGA-II algorithm and apply it to this strategy. Our experimental results on million-scale social networks validate the good performance of the proposed approach.