Network Models

Populations may be conceptualized as a diffuse (Markov) network of interacting parts—individual based, neighborhood based, or even as a set of populations embedded within a continuous matrix. As such, we may gain some valuable insights into the processes that have created genetic structure by allowing the structure itself to reveal its own topological structure. In this topic, we explore network approaches for understanding the spatail distribution of genetic structure and covariance.

Lecture Content

For this topic, I will be spending a bit of time covering the foundations of this topic as well as giving some concrete examples. To do so, you have access to the following slide set1.

Here is a recording of the network models talk I gave on Zoom.

Supplemental Materials

In addition to this lecture content, you may find the following information helpful as you go though these exercises.


Here is the activity associated with this particular topic.

Here is some code that I used to solve the questions in the activity.

  1. This is a presentation writing in Xaringan, an RMarkdown presentation type. It is an active embed in this page (and hosted elsewhere). It can be extracted my clicking on the slide and hitting the c key on your keyboard—to clone it into its own window (more commands available using ? for viewing).↩︎