Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


The goal of the course is to get introduced to key statistical network concepts and their mathematical foundations, the theory of random graphs. The course is targeted for students in mathematics, operations research, and computer science, with interest in probability, graphs, and networks. You will become familiar with basic statistical models used to model unknown network structures. You will gain insight into the type of structural network properties that can be learned from a single graph sample. You will learn to investigate how well an observed graph fits a given statistical model by comparing observed and theoretical graphlet densities. You learn to apply probabilistic inequalities for recognizing almost surely occurring events in large random systems.

Credits: 5

Schedule: 13.09.2021 - 21.10.2021

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Lasse Leskelä, Joona Karjalainen

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English


  • valid for whole curriculum period:

    The first part of the course focuses on random graph models and their mathematical analysis. The second part focuses on the learning and identifiability of graph parameters and structural properties from observed network data.

Assessment Methods and Criteria
  • valid for whole curriculum period:

    Homeworks and project work

  • valid for whole curriculum period:

    Contact hours 30-40h, independent work ca 100h


Study Material
  • valid for whole curriculum period:

    Lecture notes, selected research articles, supplementary online material.

Substitutes for Courses


Further Information
  • valid for whole curriculum period:

    Teaching Period:

    2020-2021 Autumn I

    2021-2022 Autumn I

    Course Homepage:

    Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu ( instead of WebOodi.