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Intro

Welcome to the wiki for the course Social graphs and interactions (02805) offered by the Technical University of Denmark. This is the main page, where you can access the weekly exercises. If you take a look in the side-bar, you can read about the administrative details (including a very useful course overview), assignments, books, and more.

The class is taught flipped classroom style, where the lecture and homework elements of a course are reversed. You'll be able to view short video lectures before (or during) the class session, so in-class time can be devoted to exercises, projects, or discussions. Check out the Before week 1 lecture to learn more.

Slack Workspace

• Here you can find the Slack Workspace!

Assignments

• Here, you will find the links to the class assignments once released.

Lectures

• Before week 1. Take a look at this page before you do anything. This class most likely works a little bit differently from other classes you've taken. The notebook explains pretty much everything - the rest will be explained during the lectures.

• Python BootCamp. Python is the key tool we use in this class. If you don't feel 100% ready this notebook offers a quick refresher course.

• Week 1: Introduction. This week is all about getting started. It's a light load, since we want everyone to get a good start, especially if you're not a Python Ninja, just yet. Thus, there's room for prep, making sure you're all on top of Python, etc. But we also get started on the Network science with an introductory lecture, and playing with NetworkX, the Python library for network analysis. In case the link below doesn't work, you can also see the file here on github, but the videos won't display properly.

  • Reading: Network Science book, Chapter 1. You can get the whole book for free here. Or buy it at the Campus Bookstore.

• Week 2: Networks I. It's time to learn a bit more about networks. I have to admit that I love networks. I could talk about them for hours. And that's actually also what I'll be doing for today's lecture. Lots of info from me + some reading for you guys. I'll answer some important questions, such as "Why would anyone care about networks" and "How can you use Python to study networks". In case the link above doesn't work, you can also see the file here on github.

  • Reading: Reading. Chapter 2, and 3 (section 3.1-3.7 ... the most important part is 3.1-3.4, so focus on that) of Network Science. You can find the entire book online here.