Network Analysis

Our motivating goal is to provide an intense yet enjoyable instructional experience that focuses on a large number of both introductory and advanced topics in network analysis. We strive to strike an equal balance between core concepts of the analytic techniques along with their practical application and interpretation when implemented with real empirical data. Our workshop is designed to provide participants with the materials and instruction needed to both develop a real understanding of network analysis techniques and to be able to thoughtfully apply these procedures to their own data.

Our workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, and health sciences.

Chapter 1. Basics of Network Analysis
1.1 Historical Overview
1.2 Representation of Networks as Graphs, Sets, and Matrices
1.3 Types of Networks
1.4 Network Data: Measurement and Collection
1.5 Survey Sampling in Networks

Chapter 2. Identifying the Most Important Actors/Elements in a Network
2.1 Short Introduction to Graph Theory
2.2 Properties of Networks
2.3 Identifying the Most Central Actors: Centrality Measures
2.4 Identifying Actors with Similar Functions: Equivalence and Balance

Chapter 3. Visualizing Network Relationships
3.1 Foundations of Visualization Methods
3.2 Visualizing Relationships between Actors
3.3 Visualizing Causal Networks: Directed Acyclic Graphs

Chapter 4. Finding Members of Networks with Similar Traits
4.1 Clustering and Graph Partitioning
4.2 Finding Densely Linked Sets of Actors: Community Detection
4.3 Comparing Solutions
4.4 Voluntary Partitioning: Assortativity

Chapter 5. Brain Network Analysis
5.1 Introduction to Brain Networks
5.2 Connecting Network Terminology to Brain Structure
5.3 Core-Periphery Partitions and Rich Clubs
5.4 Diffusion and Navigation
5.5 Small World Networks and Network Economy
5.6 Null Models for Connectivity Networks
5.7 Using and Creating Registered Networks

Chapter 6. Psychometric Network Models: Modeling Relationships Among Item Responses
6.1 Motivating Application in Psychopathology: Psychiatric Symptom Networks
6.2 Models for Continuous and Binary Data
6.3 Estimation Approaches
6.4 Advanced Topics in a Rapidly Evolving Area
6.5 Alternative Approaches to Estimation: Back to Basics

Chapter 7. Fitting Probability Models to Networks
7.1 Local Structure Models: Triad Analysis
7.2 Individual Level Models: p1 models
7.3 Network Level Models: p* / Exponential Random Graph Models (ERGMs)
7.4 Extensions to Directed Networks

Chapter 8. Longitudinal Network Models
8.1 Introducing Longitudinal Networks
8.2 Necessary Assumptions of Longitudinal Networks
8.3 Statistical Models for Network Dynamics
8.4 Tie-based Network Dynamics and Models
8.5 Actor-based Network Dynamics and Models

Appendix: Advanced Topics
A.1 Finding Similarly Functioning Sets of Actors: Blockmodeling
A.2 Formally Comparing Networks

Software demonstrations are provided in R (you can download R for free). While it is helpful to have some familiarity with R, this is not necessary. The lectures which constitute the majority of the workshop are software-independent.

Network Analysis is a five-day workshop originally taught live via Zoom by Doug Steinley. Daily lectures were held from 9:00 to 5:00 with morning, lunch, and afternoon breaks. Sessions consisted of comprehensive lectures, detailed presentation of real-data examples, response to participant questions, and general discussion. Each day concludes with a live demonstration of all examples using R.

Self-paced participants receive lifetime access to all course materials, including complete course notes, lecture recordings, software demonstration notes, software demonstration recordings, and data and code for all examples. You can revisit these materials any time you like, without worrying about expiration dates. Pretty awesome, huh?