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.
Wageningen University & Research
Currently researcher modelling at Biometris, Wageningen University & Research
modelling,
ecology, foraging, forestry, forest management, water management
scenario analysis
agent based modelling
individual based modelling
model quality, model evaluation, model governance
Research fellow, PhD Candidate (University of Kassel)
Energy system transiton modelling * stakeholder and market modelling, governance and policy modelling, * agent-based modelling (ABM), optimisation, * model coupling, open and integrative modelling framework, * open source, S4F
Flood Risk Management, Coupled Human-Natural System Modelling, Socio-hydrological Modelling, Agent-Based Modelling, Human Behaviour Modelling, Agent-Based Social Simulation, Hydrological and Hydraulic Modeling, Geographic Information Systems (GIS), Mapping, Risk Modelling and Risk Visualization, Disaster Risk Reduction