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Jonas L. Juul edited this page Oct 24, 2023 · 22 revisions

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!

Lectures

  • Week 8: NLTK III. We've reached the end of the lectures. And it's a good one! We start by finishing up the work that focuses on understanding the communities via their content by using TF-IDF based word-clouds. We then study topics in the rapper Wikipedia and learn about sentiment analysis! As usual, if the link above does not work, you can find the notebook here

  • Week 7: NLTK II, A mixed bag of useful tricks. Now, let's get real and work with some language/text. I'm taking you through the dreaded chapter 3 of NLPP, talking about TF-IDF as a way to summarize what is important about a document, and we'll be getting into sentiment.

  • Week 6: NLTK I, Getting started with NLTK. Ok. So we're changing gears. We've looked at the rapper Wikipedia network. Now we'll put together the tools for working with the text. Today is all about getting familiar with the python library nltk and getting comfortable working with text.

    • Reading: Natural Language Processing with Python (NLPP) Chapter 1, Sections 1 to 4. (It's free online) and NLPP Chapter 2 Sections 1 to 4.

  • Week 5: Networks IV, Advanced measures. You've done a lot of work retrieving the rapper networks from Wikipedia. Today the goal is to analyze it and learn something about both network science, rap music and Wikipedia itself along the way.

    • Reading: This week, the reading is mostly for reference. It's for you to have a place to go, if you want more detailed information about the topics that I cover in the video lectures. Thus, I recommend you check out Chapter 9 of the network science book. In particular, we'll delve into section 9.4 in the exercises. We will also talk a little bit about degree correlations - you can read about those in Chapter 7.

  • Week 4: Networks III, Revenge of the Data Scientist. Today you will be getting your very own dataset from Wikipedia. Working with real data is a pain in the a**, and today you will experience this fact firsthand. But you should, of course, be thanking me for providing this experience, since this is what the real world feels like. After your life at DTU, no one will be giving you a nice, cleaned dataset that you can easily load into your favorite data structure. So I hope this experience will be valuable for you as you move through life after DTU. And I promise that you will never fear raw data again. As always, in case the link above doesn't work, you can also see the file here on github.

    • Reading: There's no reading today. You'll have enough to do with running regular expressions and other fun things. So if you're bored, this is a good time to catch up on the stuff from the previous weeks that you haven't looked at.
    • Assignment: We will be posting Assignment 1 after lunch (I will let you know on Slack when it is posted). Remember to read the instructions carefully. If you are not in a group yet, now is the time to find one: the assignment should be handed in as a group.

  • Week 3: Networks II. It's time to cover the two discoveries that led to an explosion of work on networks around the change of the millennium. Two papers were published: the Watts and Strogatz paper on small-worlds networks and the Barabasi-Albert model on scale-free networks. Finally, we will talk about the configuration model, an immensely useful network model and probably my favorite one. In case the link for the lecture does not work, you can also see the file here https://github.com/SocialComplexityLab/socialgraphs2023/blob/main/lectures/Week3.ipynb .

    • Reading: Sections 3.5-3.10 with emphasis on 3.8 and 3.9; sections 4.1-4.8, and 5.1-5.5.

  • 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 will follow up on last week's pictures of networks, and touch on topics such as "How can you use Python to study networks" and "Why are models of random networks useful?". Some amazing people from industry will also tell us about why knowing about networks could be useful outside of this class. In case the link above doesn't work, you can also see the file here on github.

    • 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.

  • 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.

  • 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.

  • 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.

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