Diffusion of innovation and collective action in complex networks

Official URL: http://risc01.sabanciuniv.edu/record=b1615016 (Table of Contents)

In this thesis, we implement the existing models for the diffusion of innovation and we present a novel model for the collective action in complex networks. We also create a new network called the Robin Hood network using poor get richer approach. The di usion models are divided into three components, namely node, link and neighbor diffusion. Each diffusion model is simulated in ring lattice, small worlds, random networks and scale free networks in order to analyze the e ect of network topology. The simulation results suggest that low level of clustering coefficient and average path length, as well as the presence of hubs are efficient for the innovation to become widespread. Comparative results for the collective action show that the presence of hubs is insignificant for determining the size of the collective subset. On the other hand, the tightly-knit communities are e ective for the collective decision making. We also provide sufficient parameters for the modelling of networks where both the diffusion and collective action take place. According to our results, the clustering plays an important role in both diffusion and collective action. However, the presence of hubs is not a must for collective action.