I’m a PhD researcher at the University of Glasgow working on modelling political polarisation on social media platforms suing agent-based models
agent-based models, social networks, python, R, NetLogo
Mainly interested in studying social networks of learners, teachers, and innovators. Uses Social Network Analysis, but also sentiment analysis, data mining, and recommender system techniques.
PhD student in The Robert Zajonc Institute for Social Studies at the University of Warsaw.
network science; social networks; sociology; complex systems; ecological psychology; cognitive science; perception and action
PhD student at University of Toronto: memes, social networks, contagion, agent based modeling, synthetic populations
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.
Networks Theory, Applied Microeconomics, Industrial Organization and Social Interactions.
I have a particular interest in the way in which social network structure influences dynamic processes operating over the netowrk, such as adoption of behaviour or spread of disease. More generally, I am interested in using complex systems methods to understand social phenomena.
My main research interests are the theoretical and experimental analysis of the dynamics of social networks, in relation to problems of cooperation and conflict.
Andrew Crooks is an Associate Professor with a joint appointment between the Computational Social Science Program within the Department of Computational and Data Sciences and the Department of Geography and GeoInformation Science, which are part of the College of Science at George Mason University. His areas of expertise specifically relate to integrating agent-based modeling (ABM) and geographic information systems (GIS) to explore human behavior. Moreover, his research focuses on exploring and understanding the natural and socio-economic environments specifically urban areas using GIS, spatial analysis, social network analysis (SNA), Web 2.0 technologies and ABM methodologies.
GIS, Agent-based modeling, social network analysis