Evolution of social networks through smart phones and radio sensors

Social interactions have been an integral part of human civilization. It reflects human society and its evolution. The first challenge in the research of social networks is data acquisition. The high cost and low efficiency are always restrictions. In addition, most existing studies of network problems are using single datasets to build their network models. It is paramount to find a general method of obtaining a highly accurate network model to represent social interactions. Therefore, we propose a cross-platform system and strategy to collect data through radio sensors and design a combined scheme with multiple datasets in order to settle this problem. Moreover, we use complex network theory to build our network models. The next challenge is network dynamic. A larger number of real-world networks are dynamic, i.e. social networks, as the topology of a network changes over time. It is also hard to describe the topological variance of the network using a static network model where it does not have any time features. Thus, we propose a weighted temporal network model to illustrate the time effect of social network problems. In this study, we also analyze the effect of friendship on human social interactions and activities. The relationships among networks are shown as well. Furthermore, we show the combined network model provides a highly efficient way to construct social networks.