MTAT.03.251 Graph Mining

MTAT.03.251 Andmekaeve graafides

  • Seminars: On Tuesdays at 16:15-17:45 J. Liivi 2 - 403
  • Office hours: Mondays Liivi 2-326. Please arrange exact details of the meeting by email.
  • Questions:
  • The first seminar starts on 14:15 on 8th February
  • The course is based on the book Eric D. Kolaczyk. Statistical Analysis of Network Data.
  • Lectures and seminar sessions are held in English or in Estonian depending on circumstances.
  • Final reports are methodical web materials written in English.

What and Why

The course aims to give a overview of basic graph mining techniques starting form the graph construction and ending with prediction of graph structures. Graph mining is relatively new research area. Although graphs are natural way to represent connections and associations between objects, they are also complex discrete objects compared to simple arrays and matrices. As a result, standard statistical techniques and machine learning algorithms have to be modified to run on top of graphs. In this course, we view basic ways how to overcome this by using graph specific methods and various ways to flatten the complex structure by feature extraction. Another reason why graph mining is relatively new is purely pragmatical. Although large network structures have been present for a long time, only recent changes in technology have made it possible to collect such kind of data. For example, only recent network-oriented applications such as instant messengers, social network websites and geo-tagging, have opened an opportunity to observe and study large-scale social interactions. Also, lack of efficient algorithms and lack of raw computing power have been hindering factors. As such the topic is highly suitable for students from computer science and statistics. In particular, note that elements of graph mining are heavily used in system biology and bioinformatics and several major projects done in cooperation between department of computer science and competence center STACC are closely related to study of social networks.


There are no formal prerequisites to the seminar. However, basic knowledge in programming, basic math and graph theory is advisable. Also, one needs reasonable English skills to complete the course report. If the formal requirements of the ÕIS do not permit registration then write me an email or talk with me. After that we decide whether to enroll you or not.

To pass the course

Differently from the last years seminar, the course is organized as a reading group. That is, on average each student makes two presentations and some illustrative demos. The demos will be included into web tutorials. The course grade will be decided on the quality of presentations and web tutorials. Tutorials must contain explanation of theoretical concepts, their graphical illustrations, code snippets in GNU R that explain the concepts furthermore.

You must actively participate in most seminars or otherwise you do not pass the seminar. Namely, student gets grade F if he or she misses 3 or more seminars. In reasonable circumstances, it is possible to compensate missed seminars by extra work. Details are determined by individual agreements with the lecturer. You must do a project work. The project work will be graded in the scale from A to F and it will form the basic grade for the course.



TopicTeamPresentation date
Graph construction and visualisationEgon Elbre, Elena Nikolaeva & Roland Pihlakas22th of February
Local and global characteristicVijayachitra Modhukur & Shahab Mokarizadeh8th of March
Sampling and Estimation in Network GraphsVijayachitra Modhukur & Shahab Mokarizadeh15th of March
Small-World Models and Network Growth ModelsVijayachitra Modhukur & Shahab Mokarizadeh22th of March
Basic Methods for Link PredictionEgon Elbre, Elena Nikolaeva & Roland Pihlakas29th of March
SVM-s and Kernel methods for GraphsVijayachitra Modhukur & Shahab Mokarizadeh5th of April
Modeling Dynamic Processes in GraphsEgon Elbre, Elena Nikolaeva & Roland Pihlakas19th of April
Gravity ModelsEgon Elbre, Elena Nikolaeva & Roland Pihlakas26th of April
Tree reconstruction algorithmsEgon Elbre, Elena Nikolaeva & Roland Pihlakas3rd of May

Web Tutorials

Graph construction and visualisationElena 
Local and Global characteristicsChitra 
Small-World Models and Network GrowthSahab 
Sampling and Estimation In Network GraphsSahab 
SVM and Kernel methods for GraphsChitra 
Basic Methods for Link PredictionRoland 
Modeling Dynamic Processes in GraphsEgon 
Markov FieldsEgon 
Gravity ModelsElena 
Tree Reconstruction AlgorithmsRoland 
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