Displaying 10 of 14 results social network clear
Agent-based modelling and Social Network Analysis
GIS enthusiast and ABM practitioner
Urban Mobility
Machine Learning
Social Network Analysis
Crime Simulation
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.
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
Social scientist based in Milan, Italy. Post-doctoral researcher in Sociology at the Department of Social and Political Sciences of the University of Milan (Italy), member of the Behave Lab. Adjunct professor of Social Network Analysis at the Graduate School in Social and Political Sciences of the University of Milan.
Science, technology, and innovation policy; development policy; higher education policy; international research collaborations and networks; social network analysis; bibliometric analysis
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.
Peter Gerbrands is a Post-Doctoral Researcher at the of Utrecht University School of Economics, where is develops the data infrastructure for FIRMBACKBONE. He teaches data science courses: “Applied Data Analysis and Visualization” and “Introduction to R”. His research interests are agent-based simulations, social network analysis, complex systems, big data analysis, statistical learning, and computational social science. He applies his skills primarily for policy analysis, especially related to illicit financial flows, i.e. tax evasion, tax avoidance and money laundering and has published in Regulation & Governance, and EPJ Data Science. Prior to becoming an academic, Peter had a long career in IT consulting. In Fall 2023, he is a Visiting Research Scholar at SUNY Binghamton in NY.
agent-based simulations
social network analysis
complex systems
big data analysis
statistical learning
computational social science
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.
Current main research interests are concerned on diffusion of ICT among social actors of territorial systems: citizens(individuals and households), enterprises and governmental bodies. Most used methodological tools are , so far, multivariate statistics and Social Network Analysis.
I’d like to apply an ABM approach in the context of my PhD research project, aimed to observe the different modes of collaboration among universities and enterprises and tehir different effectiveness in terms of creation and spread of new knowledge.
Displaying 10 of 14 results social network clear