This course discusses advanced topics from the field of network science. It builds on the topics that have been discussed in Web Science and Web Technology (707.000) course. Among other we will discuss the topics of network evolution and the connection between network structure and its function.

In recent years a new multidisciplinary research field Network Science emerged from various traditional fields such as
computer science, physics, social science, or information theory. The researches in many fields recognized the nessecity to investigate
not only properties of individuals but also their connections with other individuals. For example, the famous "Six Degrees of Separation"
phenomenon from social sciences can be only explained by the existence of specific structural properties of **
social networks.** Yet another example involves e.g. the success and growth of technologies such as the Internet or the Web.
This fastest growth of any technology in the human history can be explained by simple dynamic properties (e.g. preferential attachment)
of the **network representations** of the Internet and the Web.

In this course we will *investigate* and
*discuss* some of such advanced questions in modern networks. In particular, we will
mostly deal with *information networks.*

- Denis Helic (website)

Course topics include:

- Function and structure of complex networks
- Routing function in complex networks
- Decentralized search
- Epidemics in complex networks
- Models of information diffusion

In this course the students will:

- Understand the relation between the function and the structure of complex networks
- Learn about network functions such as routing and decentralized search
- Learn about the basic concepts of object diffusion via networks
- Understand models of disease, influence, or information diffusion

At the end of this course the students will know how to:

- To interpret different network models
- To implement a simulation model
- To apply stastistical analysis of simulation data

- 07.10.2016: Course organization / Mathematics of networks
- 14.10.2016: Mathematics of networks (cont.)
- 21.10.2016: Mathematics of networks (cont.)
- 28.10.2016: Measuring network properties
- 04.11.2016: Measuring network properties (cont.)
- 11.11.2016: Empirical analysis of networks
- 18.11.2016: Discussion of student projects
- 25.11.2016: Graph partitioning and community detection
- 02.12.2016: Graph partitioning and community detection (cont.)
- 16.12.2016: Graph partitioning and community detection (cont.)
- 13.01.2017: Function of Networks / Intro to Dynamical systems
- 20.01.2017: Epidemics
- 27.01.2017: Dynamical systems on networks
- 03.02.2017: Discussion of student projects

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.

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

- Homework 1 (Tex File) 21.10.2016 (Homework 1 due 04.11.2016)
- Homework 2 (Tex File) 11.11.2016 (Homework 2 due 02.12.2016)
- Homework 3 (Tex File) 16.12.2016 (Homework 3 due 20.01.2017)
- Homework 4 (Tex File) 20.01.2017 (Homework 4 due 03.02.2017)

You should pose questions about a particular network, e.g. is it assortatively mixed, what is the correlation between in- and out-degree of nodes, etc. You should then select network measures, calculate them for a desired network and provide answers to the questions.

Then, prepare 3 slides for the discussion:

- First slide: dataset
- Second slide: experimental setup
- Third slide: results

Send me the slides per e-mail as a PDF file until 17.11.2016 24:00. Subject of the e-mail must include [NetSci].

You should model a desired process taking place on a network, e.g. how information spreads over Twitter, the flow of passengers in a traffic system, etc. You decide on the methodology how to examine the process, e.g. by simulation, analytically, or a combined appraoch. For a desired network you perform experiments, obtain results and finally discuss the results. Alternatively, you may implement a community detection algorithm using e.g. an MCMC approach such as simulated annealing.

Then, prepare 5 slides for the discussion:

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

Send me the slides per e-mail as a PDF file until 02.02.2017 24:00. Subject of the e-mail must include [NetSci].

Points from study year: 2016