Statistical Inference on Networks

Seminar for Masters students (Summer term 2018) by Debarghya Ghoshdastidar and Ulrike von Luxburg.


Modelling and learning from networks is an important research topic in many scientific disciplines. Statistical analysis of social networks reveals the reasons for making new connections, or how information spreads among people. On the other hand, study of brain and genetic networks helps in understanding different biological processes.

In this seminar, we learn different applications, algorithms and theory related to networks. In particular, we look at how machine learning can be used to solve various network analysis problems. The second, rather high-level intention of this seminar is to learn about, get used to and practice scientific work.

Schedule for presentation

Date: June 13, 2018 (Wednesday)     Venue: Sand B116

Structure of seminar

  • Each student gets assigned one paper in the first meeting. At the end of the semester everybody has to give an oral presentation of the paper. Also, in the middle of the semester, every student has to hand in an essay that provides a critical review of the assigned paper.
  • Writing reviews: before being published, scientific papers go through a peer review process. We will learn how such a review is supposed to look like, and practice to write a review. By the middle of the semester, everybody has to hand in a written seminar essay about the paper. It is supposed to summarise the contents, evaluate the scientific impact of the work, and provide a scientific review. In this essay, you will also have to judge the scientific impact of a paper. This is not so easy, in particular if you are new to the field. We will learn what are the tricks and tools to get at least some idea about it.
  • Scientific discussions: Critically discussing scientific results is an important part of science, and it is similarly important to get used to ask questions in a talk (in a lecture as well, as a matter of fact). We are going to practice this in our block seminar. For each session, we will have a session chair who leads the discussion, the person who presents the talk, an "opponent" who plays the role of a devil's advocate (and who has read the paper as well), and many questions from the remaining participants.

Time plan

We hold the main part of the seminar as a block seminar at the end of June, with a couple of intermediate meetings.
  • April 17, 10:00-12:00, Sand A302: first meeting - overview of machine learning on networks, and to discuss organisation
  • April 22: submit paper preferences (via online form)
  • May 8, 10:00-12:00, Sand A302: general discussions about how scientific publications and peer reviews work; also, guidelines for the reviews to be submitted
  • May 28: submit reviews for assigned paper and first version of slides (send PDF via email)
  • June 12-13 (A104 Sand): all presentations as a block seminar


Prior knowledge of machine learning is not needed, but some familiarity with machine learning may help. Your Masters program can be in computer science or maths or related areas.