Fluidspread: A New Method of Maximizing Positive Influence in Online Social Networks via Fluid Dynamics

Influence maximization aims at detecting the top-k influential users in online social networks. Almost all previous models of influence spread cannot simultaneously incorporate users’ attitudes, interactions between users, and dynamic influences. However, in this study, we established a new model of influence spread via fluid dynamics, which reveals the time-evolving process for influence spread. We modeled the spread of influence as the process of fluid update based on three dimensions: the difference of fluid height, the temperature of fluids, and the difference of temperature. Moreover, we formulated the problem of maximizing positive influence and devised a Fluid-spread greedy algorithm to solve it. We conducted extensive comparisons between our approach and several baselines, and experimental results illustrate the effectiveness and efficiency of the Fluidspread model and algorithm.