CeMEAI

Social Network Analysis 

Social Network Analysis 

Social Network Analysis 

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SOCIAL NETWORK ANALYSIS
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.

Objectives
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

Assessment
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.

Para se registrar para este evento, acesse a seguinte URL: https://cemeai.icmc.usp.br/ncemeai/social-network-analysis/ →

 

Date And Time

21-10-2016 to
26-10-2016
 

Local

 

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