Influence Maximization Based on Information Preference

The empirical research shows that individuals in real social network have different preference for the information with different themes, which plays an important role in information diffusion in social network. Influence maximization is a fundamental issue to find a subset of influential individuals in a social network such that targeting them initially (e.g. to adopt a new product) will maximize the spread of the influence (further adoptions of the new product).Most previous work of the influence maximization problem doesn’t take users’ preference for information theme into account, which greatly reduces the accuracy of result. To further improve the efficiency and performance of influence maximization algorithm, we propose a two-stage L_GAUP algorithm. In the first stage, based on the node’s preference for the information theme, we can get a sub-graph. Compared with other nodes in the network, the nodes in sub-graph have higher preference values for the given information theme. Then, in the second stage, based on the greedy strategy, we find the top-k influential nods in the sub-graph. In experiments, we conduct algorithm L_GAUP, GAUP and CELF in a real word dataset douban. As for three metrics runtime, IS and ISST, experimental results show that L_GAUP outperforms the benchmark algorithm GAUP greatly.