The final version of the program can be found HERE.
Martin Kilduff (PhD Cornell, 1988) is Professor of Organizational Behavior and Director of Research at the UCL School of Management, University College London. He is former editor of Academy of Management Review (2006-08) and former associate editor of Administrative Science Quarterly (2003-2005, 2010-2016). Prior to joining UCL he held positions at Cambridge University, University of Texas at Austin, Penn State, and Insead. Martin’s work focuses on how social networks relate to personality, cognition, and emotion in organizations. His recent research on these topics is in press at Academy of Management Journal, Journal of Applied Psychology, and Academy of Management Annals.
Tomás Rodríguez is an associate professor of Economics at Universidad de Los Andes. His research interests lie within game theory and his main focus to date has been on models of interaction in networks and their applications, and models of communication and aggregation of information. Although he is an economist by training, his research has benefited significantly from the insights of other social sciences, specially sociology and psychology, and wishes to preserve and enhance this interdisciplinary perspective. His current research focuses on peer effects, network formation and social integration.
Researchers from the INTERACT group will provide you with the necessary theoretical background in a particular analysis methodology and guide you through some hands-on examples.
In this workshop we will introduce the basic concepts of Social Network Analysis and their use for research. In this session we will learn how to use UCINET as a tool for SNA. We will go from data organization, management, data characterizing and visualization through the tools of data analysis for testing hypothesis with UCINET.
Online Social Network Analysis in Python
Felipe Montes Jiménez
This workshop aims to provide tools for collecting, analyzing and visualizing data of online social networks (eg. Twitter, Facebook, Flickr). We will provide students with concepts and Python scripts for calculating simple network metrics to characterize the network structural properties, to identify influential individuals and clusters in the networks useful for designing marketing campaigns and diffusion of innovations.
Introduction to ERGMs
ERGMs (exponential random graph models) are statistical models that predict the presence/absence of ties. In this session, participants will gain an intuitive (i.e., no stats and no data) understanding of why, and when, ERGMs are appropriate tools to model social networks, what ERGMs can do, and what they cannot do. Participants will also learn the typical process used to specify, estimate, evaluate and interpret an ERGM, using an empirical example.
Introduction to agent-based computational models of social network dynamics
Agent-based computational modeling (ABM) has become a standard approach to link micro-level behavior with macro-level properties. While network theory addresses the complexity of relationships among social entities, computational approaches like ABM are a convenient tool to represent networked interactions of purposeful agents, even more when such networked interactions evolve over time. This tutorial explores the link between ABM and social network analysis, focusing on dynamics and change of the network topology as a result of agents’ decisions.
Introduction to SIENA (Simulation Investigation for Empirical Network Analysis)
SIENA models are stochastic models that allow researchers to analyze social networks over time. They focus on understanding how actors alter their relationships as a function of individual attributes and network characteristics. In this workshop, participants will receive an introduction on how to use the associated packages in R, how to estimate basic models, and how to interpret the results.
The event will feature a permanent data camp, a learning by doing network experience, in which some of our team members will be available to help with your own networks research questions.