Advanced Network Science
Networks are all around us: We are ourselves, as individuals, the units of a network of social relationships of different kinds; the Internet and the highway system can be modelled as networks embedded in space; networks can be also entities defined in an abstract space, such as networks of acquaintances or collaborations between individuals. This course aims at providing the computational tools to study these networks and form an advanced understanding of the current state of the art of network science. The final objective is to have the students master computational techniques to solve advanced network problems, to be able to contribute to the development of network analysis, and to appreciate the limitations and future developments of scientific papers dealing with network problems in real-world data.
Network science is a thriving field of study. The reason of its popularity is its ability to represent very complex phenomena with very simple models (graphs). With network science one can analyze: global societal patterns, catastrophic breakdowns of distributed infrastructures, the metabolic pathways in humans at the basis of diseases, among many examples. The topics covered during the course are:
- Basic node role classification;
- High-order dynamics – interactions between nodes that go beyond their connections;
- Deep learning on graphs;
- Graph summarization;
- Frequent subgraph mining;
- Network distances.
During the course, you will be reading classic articles from the network science literature selected by the teacher. You will choose one as the main topic for your oral examination.
The student should have an advanced understanding of statistics, linear algebra, machine learning, and Python programming.
The student must have taken the Network Analysis course offered in the BSc Data Science program, or an equivalent course covering the same topics.
If the student did not attend a network analysis course at ITU or elsewhere, they should request access to the LearnIT page of the Network Analysis course and study the materials. Specifically, they should focus on Chapters 3-11, 13-20, 23-27, and 31-34 of the textbook (https://www.networkatlas.eu/).
Intended learning outcomes
After the course, the student should be able to:
- Identify the relevant literature to follow the methodology of a network science research paper
- Reproduce the analysis of a network science research paper
- Select the most appropriate computational tool to address a specific question on a network
- Analyse real world social networks by applying advanced network analysis tools like (e.g. graph neural networks)
- Plan potential ways in which to customize an available tool to fit a new network feature or question
Ordinary examExam type:
B: Oral exam, External (7-point scale)
B1GH: Oral exam in group, with time for preparation. Home.