The material presented here is for the 4-week summer course Network Analysis I in Summer, 2017 for the Inter-university Consortium for Political and Social Research (ICPSR) Summer Program.
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README.md
Syllabus_Network_Analtyics.pdf

README.md

ICPSR: Network Analysis I

James D. Wilson

Email: jdwilson4@usfca.edu

Class Time: M-F, 1:00 - 3:00 PM in 1650 CHEM building

Office Hours: T, TH 11:30 AM - 12:30 PM in 1106 D Perry building

Syllabus: Link

Course Learning Outcomes

By the end of this course, you will be able to

  • Proficiently wrangle, manipulate, and explore network data using the R programming language
  • Utilize contemporary network R libraries including statnet and igraph
  • Visualize network data
  • Partition networks using contemporary community detection methods
  • Formulate data-driven hypotheses about relational systems using network analysis tools

Course Overview

The focus of this course will be to provide you with the basic techniques available for making informed, data-driven decisions with network data structures using the R programming language. In particular, we will discuss the following topics

  • History of Networks
  • Network Applications
  • Types of Network data: static, temporal, multilayer, directed, undirected, bipartite
  • Structural Importance and Centrality
  • Topological Summaries of Networks
  • Shortest Paths
  • Automatic Feature Learning of Graphs
  • Community Detection
  • Epidemics on Networks
  • Intro to Statistical Network Modeling (if time permits)

Schedule

Completed Assignments: Submit

Basics of R and Data Science

Topic Reading Practice In-Class Code
Intro and A Brief History of Data Science Ch. 1 of Doing Data Science Assignment 1 -
Basics of R and RStudio Ch. 2 and 4 of R for Data Science Assignment 2 Coding Basics
Data Structures in R Ch. 20 of R for Data Science Assignment 3 Lists and Data Frames

Foundations of Network Analysis: A History, Applications, and Construction

Topic Reading Practice In-Class Code
History of Network Analysis Ch 2.1 - 2.2 of Network Science - Data Frames
Network Types Ch 2.4 - 2.7 of Network Science - -
Manipulating and Visualizing Network Data Ch 2 and 3 of SAND with R - [igraph] [statnet]
Where's the Network? - [Assignment 4], [Assignment Template], [Solutions] Fitting Networks

Network Descriptions: Local and Global Summaries

Topic Reading Practice In-Class Code
Paths, Shortest Paths, and Connected Components Ch. 2.8 - 2.9 of Network Science - [Algorithm Demonstrations] [Path Examples]
Degree Distribution and Graph Counts [Ch. 2.3 of Network Science] [Ch. 4.1 - 4.3 of SAND] - Network Summaries
Community Detection [Review Paper] [Applications] - -
Structural Importance: Vertex Centrality Centrality in Social Networks - Centrality Examples
Case Study: Descriptive Analysis in R - - -
Feature Learning for Networks Intro to Machine Learning - -

Dynamics on Networks

Topic Reading Practice In-Class Code
Epidemic Models - Ch. 8.5 Watts Model - -

Additional Resources

Network Data

  • Zachary's Karate Club: A social network of friendships between 34 members of a karate club at a US university in the 1970s.
  • Political Blog: A directed network of hyperlinks between weblogs on US politics, recorded in 2005 by Adamic and Glance.
  • Les Miserables: A coappearance network of characters in the novel Les Miserables.
  • Power Grid: An undirected, unweighted network representing the topology of the Western States Power Grid of the United States.
  • Facebook Social Circles: A network of anonymized social circles from Facebook.
  • Enron Email Network: Enron email communication network where nodes are email addresses and edges are emails sent from i to j.

Important Dates

  • Tuesday, June 27th - First day of class
  • Tuesday, July 4th - Holiday, no class
  • Friday, July 21st - Last day of class