Network Analysis II: Advanced Topics

Instructor(s):

  • Olga Chyzh, Iowa State University

This workshop will be offered in an online video format.

This is a course on inferential network analysis. The conventional categorization of data analytic methods into descriptive and inferential statistics can be fruitfully applied to network analysis. Descriptive methods of network analysis are important for illuminating structural features of a given network, but they cannot be used to build and/or test theories about the generation of networks. Inferential methods of network analysis can be used to test hypotheses about the generation and evolution of a network, derive measures of uncertainty for network indices, and find probabilistic models that accurately describe the overall features of a network.

The first week will look at regression alternatives and variations in the network context which are both designed to account for network dependencies without formulating complex network statistics. We move from these models to Exponential Random Graph models (ERGMS) which can be parametrized to represent complex dependence processes and the effects of exogenous covariates. In the third and fourth weeks we will cover statistical models for longitudinal networks. These will include a longitudinal extension of the ERGM -- the Temporal ERGM, and the actor-oriented model of network dynamics (i.e., SIENA). We will present each model mathematically, discuss published social science applications of them, and utilize the models on example datasets. We will wrap up by exploring alterative network models, such as Latent Space Models (LSMs) and Local Structure Graph Models (LSGMs).

Prerequisites: Prerequisites include an introduction course in network analysis, as well as familiarity with statistical modeling.

Fee and Registration: This course is part of the second four-week session. Please see our fee chart on our Registration page for the cost of attending one (or both) four-week session(s). Participants who enroll in a four-week session may take as many courses (workshops and lectures) as desired during the session for which they are enrolled. Participants in the four-week sessions are also welcome to attend all of the lectures and discussions offered in the Blalock Lecture Series.

Tags: network, network analysis, social network, big data, system science

Course Sections

Section 1

Location: Online -- Video format,

Date(s): July 20 - August 14

Time: 10:00 AM - 12:00 PM

Instructor(s):

  • Olga Chyzh, Iowa State University

Syllabus: