Influence maximization based on network representation learning in social network

Influence Maximization (IM), an NP-hard central issue for social network research, aims to recognize the influential nodes in a network so that the message can spread faster and more effectively. A large number of existing studies mainly focus on the heuristic methods, which generally lead to sub-optimal solutions and suffer time-consuming and inapplicability for large-scale networks. Furthermore, the present community-aware random walk to analyze IM using network representation learning considers only the node's influence or network community structures. No research has been found that surveyed both of them. Hence, the present study is designed to solve the IM problem by introducing a novel influence network embedding (NINE) approach and a novel influence maximization algorithm, namely NineIM, based on network representation learning. First, a mechanism that can capture the diffusion behavior proximity between network nodes is constructed. Second, we consider a more realistic social behavior assumption. The probability of information dissemination between network nodes (users) is different from other random walk based network representation learning. Third, the node influence is used to define the rules of random walk and then get the embedding representation of a social network. Experiments on four real-world networks indicate that our proposed NINE method outperforms four state-of-the-art network embedding baselines. Finally, the superiority of the proposed NineIM algorithm is reported by comparing four traditional IM algorithms.