Community

Cristina Chueca Del Cerro Member since: Friday, May 15, 2020

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

Rory Sie Member since: Tuesday, February 11, 2014

dr., MSc.

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.

Szymon Talaga Member since: Tuesday, July 16, 2019 Full Member

MSc Psychology

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

Ian Dennis Miller Member since: Tuesday, February 16, 2016 Full Member

MA Social Psychology, BS Cognitive Science

PhD student at University of Toronto: memes, social networks, contagion, agent based modeling, synthetic populations

Xiaotian Wang Member since: Friday, March 28, 2014

PHD of Engineering in Modeling and Simulation, Proficiency in Agent-based Modeling

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.

Simone Righi Member since: Tuesday, March 17, 2015

Ph.D. In Economics

Networks Theory, Applied Microeconomics, Industrial Organization and Social Interactions.

Matteo Morini Member since: Friday, February 22, 2019 Full Member

PhD, Computer Science, ENS Lyon, MS, Economics, University of Turin

Jacopo A. Baggio Member since: Tuesday, April 27, 2010 Full Member Reviewer

PhD in International Development, MA in Development Economics, BsC in Economics and Social Sciences

Károly Takács Member since: Monday, October 20, 2014

PhD

My main research interests are the theoretical and experimental analysis of the dynamics of social networks, in relation to problems of cooperation and conflict.

Jennifer Badham Member since: Tuesday, February 10, 2015 Full Member

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.

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