The Research On Diffusion Process In Multiplex Social Networks

Recently, the issue of diffusion processes in multiplex networks has attracted more and more research interests. Because of the inherent multiple linking types between individuals, multiplex networks consist of multiple layers conjoint by cross-layers paths. Many studies have extended traditional diffusion models into multiplex networks by assigning different weights of links or diffusion probabilities in multiple layers. However, most present models generally overlook the effects of multiplex structures and mutual influences between conjoint diffusion processes in multiple layers. It means that the multiplex networks are still simplified as the traditional simplex network. In this dissertation, we focus on the effects of cross-layers diffusion and conjoint diffusion processes in the multiplex structure. Using agent based mod-eling methods, the interactions between agents in single layer and multiple layers are pre-sented in addition to the limitations of conjoint diffusion processes in the multiplex structure. The main works and contributions of this dissertation can be summarized as follows.(1) The speed up and slow down phenomena of diffusion processes in multiplex networks are analyzed. It is found that the slow-down phenomenon emerges due to the obstruc-tion of cross-layers diffusion which connects the distributed shortest path in multiple layers. Extensive simulations also show that multiplex networks generated by differ-ent network models are more competent for the diffusion process compared with mul-tiplex networks generated by a single network model.(2) Considering the cross-layers delay and intra-layer delay, a numerical model based on the mean-filed theory is proposed to grasp the macroscopic behaviors of temporal propagation dynamics in multiplex networks. The numerical model can predict the propagation in multiplex networks by a slight margin. Moreover, it is shown that the two aspects of temporal dynamics can dramatically vary the influences of multiple layers on diffusion processes.(3) A novel agent-based diffusion model is proposed to capture the cross-layers evolution of opinions in viral marketing when multiplex networks are conjoint by physical net-work and online social network. Most present studies consider the viral marketing as simplified epidemic processes and overlook the effects of opinion evolution. It is found that the consensus formation of opinion evolution is closely related to the diffu- sion process.(4) In order to promote the diffusion process of cooperative behaviors and guide agents to efficiently allocate limited resources between multiple layers, we introduce the degree diversity into multiplex networks and then design a greedy-first mechanism. Based on the effects of the degree diversity, the cooperative behaviors of hub agents can be maintained and the temptation of defective behaviors can be inhibited. The greedy-first mechanism helps to prevent the disappearance of cooperation in the low-er-payoff layer.