Introduction to Network Analysis

COURSES

INTRODUCTION TO NETWORK ANALYSIS

General Information

Aim:

This course is predominantly an applied statistical course. It aims to provide the basic theoretical and operational concepts to the student about Exploratory Social Network Analysis applied to demographic studies. The course will cover construction and use of relational data for network analysis applied to different research areas: publication networks, migration networks, cognitive networks and spatially explicit social representations. The empirical part of the course will be based on two main softwares: R and Gephi.  I expect that students read the suggested literature specific to each method, as well participate in the data laboratory classes. At the end of the course I expect students to be able to manipulate data in all programs used during the course and apply the methods to specific areas of interest in Demography, Geography, Sociology, Economics, and Health Studies. Despite the exploratory emphasis of this course, a final talk on Inference for Social Networks will overview state of art models and inferential and computational limitations.

 

Tests and Grading:

  • Assignment 1: parameters estimation and interpretation of a network (40 points) [Download]
  • Assignment 2: network visualization and interpretation (30 points)
  • Applied study: empirical application of network analysis to the student’s research area of interest (40 points)

More details: 

Download the complete syllabus here. Download pdf_icon

Data

Data_Class02_RData. download  r_symbol2

Data_Class03_RData. download  r_symbol2

Data_Class04_RData. download  r_symbol2

Data_Class05_RData. download  r_symbol2

Data_Class06_RData. download  r_symbol2

Data_Class07_RData. download  r_symbol2

Data_Class08_RData. download  r_symbol2

Scripts

Class02_R. download  r_symbol2

Class03_R. download  r_symbol2

Class04_R. download  r_symbol2

Class05_R. download  r_symbol2

Class06_R. download  r_symbol2

Class07_R. download  r_symbol2

Class08_R. download  r_symbol2

Beamers

Class01_Part01_Beamer. Download  1200px-LaTeX_logo.svg
Class01_Part02_Beamer. Download  1200px-LaTeX_logo.svg
Class01_Part03_Beamer. Download  1200px-LaTeX_logo.svg
Class01_Part04_Beamer. Download  1200px-LaTeX_logo.svg
Class01_Part05_Beamer. Download  1200px-LaTeX_logo.svg
Class01_Part06_Beamer. Download  1200px-LaTeX_logo.svg

Class03_RMarkDown. Download  1200px-LaTeX_logo.svg

Class04_RMarkDown. Download  1200px-LaTeX_logo.svg

Class05_Beamer. Download  1200px-LaTeX_logo.svg

Class06_Beamer. Download  1200px-LaTeX_logo.svg

Class07_Beamer. Download  1200px-LaTeX_logo.svg

Class08_Beamer. Download  1200px-LaTeX_logo.svg

Video Classes

 

Introduction to R and R Studio. youtubelogo

Extra Materials

 

R Markdown Cheat Sheet. r_symbol2

References

Kolaczyk, Eric D. (2009). Statistical Analysis of Network Data: Methods and Models. Springer, New York.

Kolaczyk, Eric D. and Csárdi, Gábor. (2014). Statistical Analysis of Network Data with R. Springer, New York.

Luke, Douglas A. (2015). A User’s Guide to Network Analysis in R. Springer, New York.

Pereira WHS, Guedes GR, Duarte D, Ribeiro R, Amorim MA. (2017). Social Representations and Cognitive Networks: a Graph-based approach. MGEST. download pdf_icon