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Topological Phase Transition in Network Spreading


 This paper investigates information spreading from the perspective of topological phase transition. Firstly, a new hybrid network is constructed based on the small-world networks and scale-free networks. Secondly, the attention mechanism of online users in information spreading is studied from four aspects: social distance, individual influence, content richness, and individual activity, and a dynamic evolution model of “connecting with spreading” is designed. Eventually, numerical simulations are conducted in three types of networks to verify the validity of the proposed dynamic evolution model. The simulation results show that topological structure and node influences in different networks have undergone phase transition, which is consistent with the phenomenon that followers and individual influence in real social networks experience phase transition within a short period. The infection density of networks with the dynamic evolution rule changes faster and reaches higher values than that of networks without the dynamic evolution rule. Furthermore, the simulation results are compared with the real data, which shows that the infection density curve of the hybrid networks is closer to that of the real data than that of the small-world networks and scale-free networks, verifying the validity of the model proposed in this paper.