Displaying 10 of 13 results social network clear

Sascha Holzhauer Member since: Sun, Nov 28, 2010 at 08:41 PM

Agent-based modelling and Social Network Analysis

Eo SeungWon Member since: Thu, Aug 03, 2017 at 05:09 AM Full Member Reviewer

B.A. Urban Studies, UC Berkeley., MSc. Geographic Information Science, Seoul National University.

GIS enthusiast and ABM practitioner

Urban Mobility
Machine Learning
Social Network Analysis
Crime Simulation

Rory Sie Member since: Tue, Feb 11, 2014 at 10:14 AM

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.

Özgür Kadir Özer Member since: Fri, Mar 25, 2022 at 12:07 PM

Master of Public Policy, George Washington University., Ph.D. (ongoing), Science and Technology Policy Studies, Middle East Technical University.

Science, technology, and innovation policy; development policy; higher education policy; international research collaborations and networks; social network analysis; bibliometric analysis

Andrew Crooks Member since: Mon, Feb 09, 2009 at 08:11 PM Full Member

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

Federico Bianchi Member since: Mon, Apr 14, 2014 at 09:21 AM Full Member

Ph.D., Economic Sociology and Labour Studies, University of Milan - University of Brescia (Italy), M.A., Sociology, University of Turin (Italy), B.A., Philosophy, University of Milan (Italy)

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.

  • the link between economic exchange, solidarity, and inter-group conflict
  • peer-review evaluation in scientific publishing
  • integrating Agent-Based Modelling (ABM) with Social Network Analysis (SNA)

Jennifer Badham Member since: Tue, Feb 10, 2015 at 04:31 PM 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.

Xiaotian Wang Member since: Fri, Mar 28, 2014 at 02:23 AM

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.

Alessandro Sciullo Member since: Mon, Nov 11, 2013 at 06:20 PM

Political Science

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.

Arezo Bodaghi Member since: Tue, Jan 30, 2018 at 04:45 PM

Master of science

My profound interest in networks convinced me to work in these subjects and start my master project on an application of social network analysis for detecting organized fraud in Automobile insurance, which helps to flag groups of fraudsters. The key point of this project is simply to find fraudulent rings, while the most of traditional methods have only taken opportunistic fraud into consideration. My duty in research is to design an algorithm for identifying cyclic components, then to be compared with theoretical ones. This project showed me how networks are used in the analysis of relations.

Displaying 10 of 13 results social network clear

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