Influence Maximization in Attributed Social Network Based on Susceptibility Cascade Model

Influence maximization is the problem of finding a small subset of seed nodes in a social network that can effectively maximize the spread of influence using a specific information diffusion model. Traditional methods do not often overlook the attributes of source information and the preference of users in the network, which can result in significant deviations. In this paper, we design a susceptibility cascade model to address the trade-off between influence maximization and the diffusion process. More specifically, we extend the original independent cascade model and propose a more realistic susceptibility cascade model to simulate the information diffusion process. Considering diverse source information attributes and user preferences, we propose an influence maximization algorithm based on the susceptibility cascade model and reverse reachable sampling, and its improvement, that incorporates the community structure. Comprehensive experiments on two real-life social networks obtained from publicly available datasets demonstrate the effectiveness of our algorithms in maximizing the spread of influence.