Actor-based Dynamics of Longitudinal Networks
This research aims to develop new method and algorithm for capturing actor-based dynamics of longitudinal networks.
Study of the dynamics of longitudinal networks has been the subject of intense interest in recent years because it provides a way to understand the micro mechanisms in the process of network formation and development, and to explore the dynamics of actor-level participation within networks over time. Intuitively, there are mainly two approaches to study the dynamics of longitudinal networks: (i) network-level; and (ii) actor-level. The methods (e.g., exponential random graph model and stochastic actor-level model) used for network-level approach have the weakness of computational complexity. Also, these methods cannot quantify the contribution of individual actor in the change of network dynamics over time. Based on actor-level approach, this research project therefore aims to develop a framework for capturing actor-level dynamics of longitudinal networks, which can overcome the drawbacks of network-level approach. In actor-level approach, the dynamic behavior (such as, activity of actor, actor popularity, level of involvement, and embeddedness of actors in a network) of individual actor over time is studied and analyzed. The outcomes of this project could enhance our present knowledge about how to break, control, or destroy a network, which is a very important research topic in some research contexts such as ‘Drug Users’ Network’, ‘Military Network in War Field’, and ‘Disease Spread Network’. Moreover, better understanding of actor-based contribution to network evolution could potentially contribute in many areas of ‘Network Science’ such as ‘link prediction’ and ‘community detection’.
Want to find out more?
The opportunity ID for this research opportunity is: 1564
Other opportunities with Dr Shahadat Uddin