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Network Analysis Textbook

08 INFERENCE FOR NETWORKS – DYAD MODEL OVERVIEW (2017)

08 Inference for Networks – DYAD Model Overview (2017) from Duke Network Analysis Center

Feel free to watch the 2017 lectures

<https://youtu.be/5gPXwfIwHLs?list=PLwrOoPWTJPLQomWt4WQuAvrhihQkXoTPV

More presentations

00 Social Influence Effects on Men’s HIV Testing

00 Introduction to SN&H: Key Concepts and Overview

01 Network Data Collection

02 Network Canvas

04 Ego Network Analysis

05 Whole Network Descriptive Stats

06 Community Detection

07 Network Visualization

08 Statistical Models for Nets I, cross-section

09 Diffusion Models & Peer Influence

10 Network Experiments

11 Siena Models for Selection & Influence

12 SN&H Keynote: Thomas Valente, USC/a>

R Labs

Data management / R intro

Data entry/org/clean

Ego network (1)

Ego network (2)

Descriptive stats

Network viz & communities (1)

Network viz & communities (2)

Diffusion & ergm/stat (1)

Diffusion & ergm/stat (2)

More presentation

00 Partner or Perish? Public Health Delivery Systems & Cancer Screenings for the Uninsured under the ACA (2017 Fellow)

00 Using Social Network Analysis to Inform HIV Prevention and Treatment Efforts in Tanzania (2017 Fellow)

00 Examining the Dynamic Spread of Marijuana Use in a Social Network with Community Structure (2017 Fellow)

00 Lean on my Network: Depressive Symptoms, Physical Health, and Gender in Different-Sex Older Adult Couples (2017 Fellow)

01 Network Data Collection (2017)

03 Communities in Networks (2017)

04 Data Visualization (2017)

05 Network Canvas (2017)

06 Regression with Networks – EGO Networks and Randomization (2017)

07 Applications of Diffusion (2017)

08 Inference for Networks – DYAD Model Overview (2017)

09 Inference for Networks – Exponential Random Graph Models (2017)

10 Siena Models for Selection & Influence (2017)

11 Keynote (2017)

R Labs

Installing Libraries

Data Management

Descriptive Statistics

Data Visualization

Diffusion Simulations

Latent Space Models

Add Health Network (supplement)

Data Files

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