The Uppsala University Information Laboratory
Available thesis projects
At our lab we host a limited number of thesis projects. Those working on these projects become temporary members of the lab, are expected to complete the project under the agreed time constraints (typically 2.5 months for bachelors and 5 months for masters) and to actively participate in the lab activities, so that they can contribute to information sharing and knowledge development. It is typically expected that each student moderates at least one of our fika meetings.
This is the list of currently available topics. If you are interested, please send an email to firstname.lastname@example.org with your transcript and a short motivation.
Stochastic blockmodeling for temporal network clustering
Level: Bachelor or Master
The objective of this project is to develop and test clustering algorithms for temporal networks. The algorithms will be implemented as part of an existing library (C++) and tested on real data. For the master version, the extension of existing methods is also an objective. The focus of this project, which follows an already active master thesis exploring other clustering methods for temporal networks, is on stochastic blockmodeling, which is one of the main general approaches for pattern detection in networks. The candidate must be fluent in programming, able to digest non-trivial mathematics, motivated, independent and creative.
Null models and network rewiring in temporal network clustering
The objective of this project is to implement network randomisation methods and apply them to test existing clustering algorithms for temporal network clustering. The underlying idea is that a clustering algorithm should identify patterns in the data that are not just the outcome of randomness. Therefore, one step to test a clustering algorithm is to randomise the data according to various methods to remove some aspects of the data (a typical example: moving around the times when people meet in a temporal social network) and observe whether this affects the ability to detect patterns and/or the detected patterns. Knowledge of C++ is required.
Network embedding for multiplex/text networks
Level: Bachelor or Master
Network embedding is a process that turns each node in a graph into a point in a multidimensional space, so that traditional machine learning and data mining algorithms can then be applied. This thesis concerns the implementation, testing and (for the master version) extension of network embedding algorithms for (multi)graphs extended with additional information, e.g., edge types, time and text. Knowledge of C++ is required.