An influence maximization method based on crowd emotion under an emotion-based attribute social network

Most research on influence maximization focuses the network structure features of diffusion process but lacks consideration multi-dimensional characteristics. This paper proposes attributed based crowd emotion, aiming to apply user’s emotion and group study characteristics information propagation. To measure interaction effects individual emotions, we define user power cluster credibility, propose a potential discovery algorithm aggregation mechanism locate seed candidate sets. A two-factor propagation model is then introduced, which considers complexity real networks. Experiments real-world datasets demonstrate effectiveness proposed algorithm. The results outperform heuristic methods are almost consistent with greedy yet improved time performance.