Complex Network Analysis: Tools for Economic Development

Course Overview

Network Analysis teaches you how to make sense of a complex world teeming with big systems of interconnected agents. You will learn how to model and predict social behavior at a large scale, without compromising the detailed interaction structures that are increasingly observed in growing datasets. Understanding these complex systems is crucial to construct effective policies that promote economic development in poor and developing regions.

More specifically, we will analyze how social and economic outcomes rely on the shape and structure of networks, and we will design policy recommendations that feed on rich network data to describe the best course of action. Complex Network analysis can be applied to study military alliances, the evolution of pandemics, sexual behavior among adolescents, insurance transfers within villages, road networks and congestion, or Facebook networks of (mis)information, among many other examples that pertain to the field of development economics.

The course combines theoretical/mathematical modeling and manipulation/analysis of social network data, focusing primarily on R as the preferred programming environment. Students will learn innovative concepts, models and algorithms that can be widely applied to different contexts and that provide a comprehensive toolbox to sustain and enrich applied empirical work.

Focusing on specific data sets, students will learn, among other things, how to build and plot graphs from raw data, compute centrality measures, simulate random graph models, and apply and interpret the results of community detection methods. Once these tools have been trained, we will learn how to apply them to recent topics in economic development, paying special attention to the latest field experiments and interventions in this topic. We will also learn about optimal seeding techniques that target policy effectively on complex systems such as real-world networks.

The course targets graduate students and/or professionals with an interest in network data and how it can be used to inform development policy.