ABSTRACT
This chapter introduces several methods of transforming networks into time series. It analyzes the long-range correlation and the multiscale feature of time series transformed from social networks to show their dynamical properties beyond purely topological viewpoints. Substantial progress has been made in the area of network science, among which two representatives are the small-world phenomenon and the scale-free property. The chapter provides a conclusion and discussion regarding the benefits of analyzing social networks from the time series perspective. It discusses the finite-memory random walk traversing the network and define its trajectory as a time series. The chapter shows a schematic illustration from the synthetic network to time series based on a finite-memory random walk approach. It also shows that the transformed time series can provide with abundant structural and functional properties of synthetic and social networks.
