A huge amount of information, created and forwarded by millions of people with various characteristics, propagates through online social networks every day. Understanding the mechanisms of information diffusion over social networks is critical to various applications, including online advertisements and website management. Differently from most existing works in this area, we investigate information diffusion from an evolutionary game-theoretic perspective and try to reveal the underlying principles dominating the complex information diffusion process over heterogeneous social networks. Modeling the interactions among the heterogeneous users as a graphical evolutionary game, we derive the evolutionary dynamics and the evolutionarily stable states (ESSs) of the diffusion. The different payoffs of the heterogeneous users lead to different diffusion dynamics and ESSs among them, in accordance with the heterogeneity observed in real-world data sets. The theoretical results are confirmed by simulations. We also test the theory on the Twitter hashtag data set. We observe that the evolutionary dynamics fit the data well and can predict future diffusion data.

Due to the proliferation of smart devices, internet-of-things (IoT) devices, and device-to-device (D2D) communications, the amount of mobile traffic is ever-growing. To satisfy this skyrocketing wireless data traffic demand of mobile users, the densification of the network is unaviodable. However, the denser network not only improves the transmission rate, but also increases the impact of interferences. Therefore, careful deployment of base stations (BSs) is needed to guarantee the communication quality. This chapter provides insights into the deployment of BSs in the multi-layer ultra dense network (UDN). Throughout this chapter, we will model the channel between the BSs and a typical user equipment (UE) by considering the antenna height of the BSs and derive the expressions for the interference, the coverage probability, and the area spectral efficiency (ASE) of our considered multi-layer UDN using stochastic geometry. Through numerical results, we will show how the network performance can be maximized by selecting the proper antenna heights and the densities of BSs in the multi-layer UDN.