Social network analysis has an especially long tradition in the social science. In recent years, a dramatically increased visibility of SNA, however, is owed to statistical physicists. Among many, Barabasi-Albert model (BA model) has attracted particular attention because of its mathematical properties (i.e., obeying power-law distribution) and its appearance in a diverse range of social phenomena. BA model assumes that nodes with more links (i.e., “popular nodes”) are more likely to be connected when new nodes entered a system. However, significant deviations from BA model have been reported in many social networks. Although numerous variants of BA model are developed, they still share the key assumption that nodes with more links were more likely to be connected. I think this line of research is problematic since it assumes all nodes possess the same preference and overlooks the potential impacts of agent heterogeneity on network formation. When joining a real social network, people are not only driven by instrumental calculation of connecting with the popular, but also motivated by intrinsic affection of joining the like. The impact of this mixed preferential attachment is particularly consequential on formation of social networks. I propose an integrative agent-based model of heterogeneous attachment encompassing both instrumental calculation and intrinsic similarity. Particularly, it emphasizes the way in which agent heterogeneity affects social network formation. This integrative approach can strongly advance our understanding about the formation of various networks.

Complex adaptive systems, complexity, systems science, creativity, data mining, machine learning, economic and health systems, science education

I use agent-based systems, stochastic process, mass balance models and computational statistics in exploring human exposure assessment.

I’m interested in inter-individual interactions in general, demo-genetics and group behaviour. I’m currently working on locusts. Visit my website for more info.

My PHD project focuses on understanding factors influencing individual sustainable consumption behaviour and how these factors could promote a sustainability transition.

Movement Ecology
Species Interactions
Ecological modelling

Agent based modelling;
Land use/land cover change;
Payment for ecosystem services;
Bayesian Network;
System Dynamics

My main research interests are agent-based modeling, simulation of social complexity, computational social choice, distributed systems and applied artificial intelligence.