Thesis Topics
Please feel free to contact us if you are interested in writing a bachelor or master thesis with us. Below you can find a (non-exhaustive) list of open thesis topics:
A Systematic Evaluation of Graph Embeddings for Supervised and Unsupervised Link Prediction and Graph Reconstruction
- Investigate effect of hyperparameter tuning for specific data sets
- Check how well different embedding techniques generalize with the same default parameters
Robustness of Graph Embeddings in Uncertain Network Data
- Check how graph embeddings are affected by missing or spurious edges
- Test in different graph learning tasks
Optical Network Recognition
- Develop a method to reconstruct networks from images/plots
- Support for node colors (communities) + vertex labels, edge weights, etc.
- Investigate methods to support curved edges
- Evaluation in different plots generated by different layout algorithms
German AI Map
- Creation of an interactive zoomable map of researchers in AI and Data Science using DBLP data
- Support for Citation and Collaboration networks
pathpy-torch_geometric API
- Functions to convert to/from torch-geometric data sets
- Support for temporal and heteregeneous graphs
- pathpy as bridge to load networks (incl. attributes) from network repositories (e.g. Netzschleuder, KONECT etc...)
Higher-order-network models and ML applications
- Generative models for higher order networks
- Statistical inference of higher order network representations
- ML applications of higher order models (classification, ranking, clustering, representation learning etc.)
Exploring feature space for graph classification and embeddings.
- Include graph features into ML methods for graphs
- Methods to be explored include graph neural networks, graph embeddings, random forests and SVMs
- Potential features include spectral features of graph operators, graphlets and other higher order features
- Test and compare to existing methods on benchmarks
