  Cursuscode   INFOMNWSC 
   
     
After completing this course, the student will have:
 knowledge of important (random) graph models and properties for networks
 knowledge of important dynamics of networks
 knowledge of important processes on networks
 knowledge of important algorithmic challenges and solutions for the analysis of very large networks
 the ability to analyze properties of random graphs Ability to analyze properties of dynamics and processes on networks
 the ability to survey literature of an advanced topic in network algorithms
 the ability to experimentally study an advanced topic in network algorithms
 the ability to hold a brief presentation of an advanced topic in network algorithms.
 the ability to provide/use feedback to/from peers.
Assessment
Minitests (25% of the final mark), term paper (55%), flash talk (10%), peer reviews (10%).
Prerequisites
The course assumes that you have basic skills in algorithms and mathematics: familiarity with basic graph algorithms (shortest paths, flows), such as offered in INFOAL Algoritmiek, and basic understanding of NPcompleteness, such as offered in INFOAL or INFOMADS Algorithms for Decision Support. Having taken INFOAN Algorithms and Networks is very helpful, but not required. During the class, we also work with basic probabilities and some integrals.



Network science is an exciting new field that studies large and complex networks, such as social, biological, and computer networks.
The class will address topics from network structure and growth to the spread of epidemics. We study the diverse algorithmic techniques and mathematical models that are used to analyze such large networks, and give an indepth description of the theoretical results that underlie them.
Some topics are random graphs, giant components, power laws, percolation, spreading phenomena, community detection, basic algorithms for network science, lower bounds and advanced algorithms for polynomialtime problems, sampling algorithms, streaming algorithms, sublinear algorithms, and graph partitioning algorithms.
Course form
Lectures, tutorials, term paper, peer feedback.
A major component of the class is for students to perform their own experimental study on algorithms for the community detection problem.
The results of this study will be presented in the term paper. This term paper will be reviewed by your student peers and you will present it in a joint seminar at the end of the course.
Literature
Recommended:
 A. Barabasi, "Network Science", for free online
 M.E.J. Newman, "Networks", 2nd edition (2018).
The class is mostly based on the Barabasi book, with some parts taken from Newman. Using either book is sufficient for the class.





   

 
 