Locality and Attachedness-based Temporal Social Network Growth Dynamics Analysis A case study on evolving nanotechnology scientific networks

The rapid advancement of nanotechnology research and development during the past decade presents an excellent opportunity for a scientometric study because it can provide insights on the dynamic growth of the fast evolving social networks associated with this exciting field. In this paper we conduct a case study on nanotechnology in order to discover the dynamics that govern the growth process of rapidly advancing scientific collaboration networks. This paper starts with the definition of temporal social networks and demonstrates that the nanotechnology collaboration network, in resemblance of other real-world social networks, exhibits a set of intriguing static and dynamic topological properties. Inspired by the observations that in collaboration networks new connections tend to be augmented between nodes in proximity, we explore the locality factor and the attachedness factor in growing networks. In particular, 2 Haizheng Zhang et al. we develop two distance-based computational network growth schemes, namely DG and DDG. The DG model considers only locality element while the DDG is a hybrid model that factors into both locality and attachedness elements. The simulation results of these models indicate that both clustering coefficient rates and the average shortest distance are closely related to the edge densification rates. In addition, the hybrid DDG model exhibits higher clustering coefficient values and decreasing average shortest distance when the edge densification rate is fixed, which implies that combining locality and attachedness can better characterize the growing process of the nanotechnology community. Based on the simulation results we conclude that social network evolution is related to both attachedness and locality factors.