Influence Maximization in Social Media: Network Embedding for Extracting Structural Feature Vector

In parallel with the development of online social networks, the number of active users in these media is increased, which mainly use these media as a tool to share their opinions and obtaining information. Propagation of influence on social networks arises from a common social behavior called "mouth-to-mouth" diffusion among society members. The Influence Maximization (IM) problem aims to select a minimum set of users in a social network to maximize the spread of influence. In this paper, we propose a method in order to solve the IM problem on social media that uses the network embedding concept to learn the feature vectors of nodes. In the first step of the proposed method, we extract a structural feature vector for each node by network embedding. Afterward, according to the similarity between the vectors, the seed set of influential nodes is selected in the second step. The investigation of the results obtained from applying the proposed method on the real datasets indicates its significant advantage against its alternatives. Specifically, the two properties of being submodular and monotonic in the proposed method, which lead to an optimal solution with the ratio of (1-1/e) approximation, make this method considered a tool with high potential in order to address the IM problem.