Scale Free Network Analysis of a Large Crowd through Their Spatio-Temporal Activities

Many real world complex networks from different domains share a common property that their node connectivity shows a scale-free power law behavior. In such networks, highly connected nodes (Hubs) are widely believed to have special importance in network management. In this paper, we discuss an environment whereby members of a very large crowd gathered to perform spatio-temporal activities, interact with different services and with one another to form a network of interest. The context of users is captured through smartphones and is processed by a cloud based framework to identify the aforementioned Hubs. We show that initial results exhibit Scale Free Network (SFN) behavior that can be further utilized for instant diffusion of important messages within the network through successive allocation of Hubs. We will focus on two basic network analysis metrics, in particular, degree of nodes and their weighted links. We will show that weighted links are closer to have a SFN behavior. We also plan to validate the effectiveness of our proposed SFN crowd behavior during next year Hajj, where millions of pilgrims will get together to perform religious rituals.