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
Annie Waldherr is a postdoctoral researcher at the Free University of Berlin, Institute for Media and Communication Studies. In 2012, she received her PhD for her dissertation on the dynamics of media attention. Her research interests include modeling public spheres, political online communication as well as science and technology discourses.
Social Network Analysis
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
I’m researching agent-based modeling from a social perspective – to see how people
Farzaneh Davari is a social science researcher who has worked in many diverse fields, including agriculture, conflict, health, and human rights, just to name a few. Currently, she is a Ph.D. candidate in Computational Social Science, focusing on social-ecological complex systems and applying computational science and Agent-Based Modeling to understand resilience procedure through self-organizing and learning. Meanwhile, she is a designer and instructor of the online graduate level course of Decision-making in Complex Environments in Virginia Tech.
Social-ecological complex system, resilience-building, conflictual environment
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.