Community

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.

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

Andrew Crooks Member since: Monday, February 09, 2009 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

Arezo Bodaghi Member since: Tuesday, January 30, 2018

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.

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.

Sascha Holzhauer Member since: Sunday, November 28, 2010

Agent-based modelling and Social Network Analysis

Shipeng Sun Member since: Monday, September 09, 2013 Full Member Reviewer

PhD

Hang Xiong Member since: Tuesday, February 14, 2012 Full Member

PhD

Research fellow at the Agricultural Economics and Policy Group at ETH Zurich.

Malik Koné Member since: Thursday, January 21, 2016

Master in mathematics and didactics

Agent Based Modeling (ABM), Agent Based Social System (ABSS), Multi-Agent Systems (MAS), Bayesian learning, Social networks Analysis (SNA), Socio ecological Dynamics.

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.

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