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. Bachelor/Master students get a desk in our lab and are expected to be present (and work :) ) full time, that is, around 40 hours per week; we typically collaborate with maximum 3 students in parallel. A high degree of independence, a good level of ambition and good linguistic skills (English) are necessary, as all projects are part of the research activities of the lab and are expected to contribute to it with new knowledge, algorithms, code, etc. It is also expected that each student moderates at least one of our fika meetings. For most projects knowledge of C++ is expected. For master projects (and some of the bachelor projects) knowledge of data mining/machine learning is expected.
This is the list of available topics for the Spring 2018 term. The list will be updated during the summer with new projects for the Fall 2018 term, in particular on the analysis of probabilistic networks, analysis of (temporal) text networks and analysis of online (political) data. If you are interested, please send an email to email@example.com with your transcript and a short CV or motivation.
Stochastic blockmodeling for temporal network clustering (assigned)
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.