This workshop aims to introduce inferential statistical models for network data. The workshop will integrate theoretical discussions with technical breakdowns, practical examples, and software code to perform analyses.
Just like any other area of statistics, network analytic procedures can be divided into two categories – descriptive and inferential. We will spend a short amount of time covering some descriptive basics (e.g. measures of centrality), but the emphasis of the workshop is on inferential network analysis. Methods of descriptive network analysis are suitable for many worthwhile research pursuits, but are inadequate for research problems that demand precise hypothesis testing with network data, or stochastic simulation of network processes. Within the last 20 years, methodological research on inferential network analysis has seen several groundbreaking innovations in model formulation/specification and computation. The focus of this workshop is to cover the most important of these innovations theoretically, through example, and then get some practical experience working with their implementations. Slides | Codes and Sample Datasets
Date: November 10, 2022 [US Central time] | November 11, 2022 [Taiwan]
Time: 19:00 - 21:00 [US Central time] | 09:00 - 11:00 [Taiwan]
Speaker: Dr. Skyler Cranmer