Actor-based Dynamics of Longitudinal Networks
Summary
This research aims to develop new method and algorithm for capturing actor-based dynamics of longitudinal networks.
Supervisor(s)
Research Location
Program Type
Masters/PHD
Synopsis
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’.
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Keywords
actor-based dynamics, longitudinal networks, network dynamics
Opportunity ID
The opportunity ID for this research opportunity is: 1564
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