706.616 Computational Social Systems 1

Graz University of Technology, Summer 2019
Classes: Thursdays 12:15 - 13:45, March 2020 - June 2020, Room HS i8 (Inffeldgasse 13)
TUGonline: https://online.tugraz.at/tug_online/pl/ui/$ctx/wbLv.wbShowLVDetail?pStpSpNr=228648 *

* 16.03.2020: register for the course!

* If you do not register you can not obtain the grade for this course!

Instructors (alph. ordered):

When sending e-mails, please remove spaces in e-mail addresses, replace <8 8 8> with @, and start your subject line with [css1]

Denis Helic

dhelic <8 8 8> tugraz.at

Elisabeth Lex

elisabeth.lex <8 8 8> tugraz.at

About the course

This course aims to provide students with a basic understanding and knowledge about the structure and analysis of social networks, selected web phenomena, including topics such as the small world problem, homophily, network dynamics, preferential attachment and others.


Homework

There will be 2 homework assignements in this lecture. Each assignment consists of 2 programming/applied mathematics problems.

Project

Please note that these are only suggestions, the choice of the topic is up to you. We will discuss further suggestions during the lectures. For example, you can (i) model a process taking place on a network, e.g. information spreading over Twitter, the flow of passengers in a traffic system, etc; (ii) analyze empirical network(s); (iii) implement a random network generator, e.g. for small world networks. You should also try to come up with your own idea. You decide on the methodology, e.g. by simulation, optimization, statistical inference, analytical, or a combined appraoch. For a desired network you perform experiments, obtain results and finally discuss the results.

Then, prepare 5 slides for the discussion:

  • First slide: Introduction/Motivation
  • Second slide: Methodology
  • Third slide: Experimental setup
  • Fourth slide: Results
  • Fifth slide: Discussion

Upload the slides in TeachCenter as a PDF file until 23.06.2020 24:00. Here, you may find suggestions for software and datasets.

Grading

Projects and excercises will be discussed during lectures. We will try to find projects which are interesting and funny for both students and us ;-)

The total number of points that can be reached will be 80 (2x25 for homework + 30 for project).

The grading scheme is as follows:

  • 0-40 points: 5
  • 41-50 points: 4
  • 51-60 points: 3
  • 61-70 points: 2
  • 71-80 points: 1

Preliminary course schedule

Note to students: Changes to this schedule will likely be made.

Note to instructors: All teaching materials on this website are available for use under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 Austria License, except for referenced material.

Lectures Date Title and Links
Lecture 1 05.03.2020 Introduction and Motivation
Lecture 2 12.03.2020 Networks I
Lecture 3 19.03.2020 Networks II (Random Graphs)
Lecture 4 26.03.2020 Small World Phenomenon I
Lecture 5 02.04.2020 Small World Phenomenon II
Lecture 6 23.04.2020 Power Laws and Preferential Attachment I
Lecture 7 30.04.2020 Power Laws and Preferential Attachment II
Lecture 8 07.05.2020 Network Dynamics I (Bayesian Learning, Information Cascades
Lecture 9 11.05.2020 Network Dynamics II (Agent-based Modeling)
Lecture 10 14.05.2020 Network Dynamics III (Opinion Dynamics)
Lecture 11 28.05.2020 Information Networks I (Hubs and Authorities)
Lecture 12 04.06.2020 Information Networks II (PageRank)
Lecture 13 18.06.2020 Project Presentations I
Lecture 14 25.06.2020 Project Presentations II

Python Examples

NetLogo Examples

Jon Kleinberg's book:

There is no required text book for this course, however this book (also available online for free) is stronly recommended:

Networks, Crowds, and Markets: Reasoning About a Highly Connected World, by David Easley and Jon Kleinberg, 2010

Further selected course material available online:

Further material:

Books:
Free online books:
Tutorials and reviews: