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Social Network Analysis

Social Network Analysis
Sex, 21. Outubro 2016, 19:00 h - Qua, 26. Outubro 2016, 22:00 h
ICMC - São Carlos - São Carlos, São Paulo



Instructor: Soong Moon Kang

Associate Professor, University College London

Course Overview

The purpose of this *project* course is to introduce fundamental concepts and methods of social network analysis for research students and academics. Social networks are social structures made up of a set of actors (such as individuals or organizations) and ties between these actors. Social network analysis provides a rigorous way of analyzing these structures to identify local and global patterns, locate influential entities, and examine network dynamics. Coupled with the emergence of online social media and large-scale data availability in businesses and society, this course will also cover the analysis of massive networks.

Over the past years, social network analysis has had significant impact in almost all fields of social sciences (including sociology, psychology, economics and anthropology), natural sciences (including biology and physics), business (including marketing, finance and operation management), engineering (including computer science and information science) and medicine (including, epidemiology and neuroscience).

This course will cover all the essential concepts, theories and data collection issues to enable participants to analyze networks relevant to their research. Specifically, the course will cover topics such as:

  • types of networks
  • data collection and large datasets
  • centrality/centralization and power law
  • structural equivalence and blockmodeling
  • brokerage and structural holes
  • cliques, community detection and small worlds
  • structural balance and transitivity
  • network dynamics and information cascades

as well as advanced topics that are relevant to student projects.


By the end of this course students should be able to:

  • understand the key tools and concepts to analyze social networks
  • understand issues related to social network data collection and analysis
  • identify and discuss theoretical and methodological issues in social
    network research

The course will be delivered in Portuguese


The course grade will be 100% based on the quality of the research proposal.

Required Text

Stephen P. Borgatti, Martin G. Everett and Jeffrey C. Johnson (2013): Analyzing
Social Networks. Thousand Oaks, CA: Sage

Session Plan (each session has about 3.5 hours duration)
Session 1 – Lecture Session: Introduction, Types of Networks, Data Collection

Session 2 – Lecture Session: Fundamental Concepts, Centrality/Centralization, Power Law

Session 3 – Lecture Session: Structural Equivalence and Blockmodeling, Brokerage and Structural Holes

Session 4 – Lecture Session: Core-Periphery, Cohesive Subgroups, Community Detection, Small Worlds

Session 5 – Lecture Session: Dyadic and Triadic Relationships, Structural Balance and Transitivity, Network Dynamics and Evolution, Information Cascades

Session 6 – Lecture Session: Egocentric Networks, Cognitive Issues, Additional Topics on Data Collection Discussion Session: Project Selection

About the Instructor

Soong Moon Kang is an Associate Professor at University College London School of Management. His research interests include social network analysis, complex systems, computational social science, social psychology, innovation and creativity, organization theory, business strategy and entrepreneurship. His research has appeared in leading international academic publications including the Proceedings of the National Academy of Sciences (PNAS), Organization Science and Social Networks as well as in main popular press such as The Economist, BBC, Der Spiegel and American Scientist. He holds a Ph.D. in Management Science and Engineering, a M.A. in Sociology and a M.S. in Engineering-Economic Systems from Stanford University, and a degree of Diplom-Ingenieur in Mechanical Engineering from Technische Universität Berlin.


ICMC - São Carlos
Av. Trab. São-Carlense
São Carlos
São Paulo
ICMC - São Carlos