Category: Theses
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Graph Representation Learning for Predicting Solvation Parameters of Ionic Liquids
This project develops Graph Machine Learning models to predict Kamlet–Taft solvation parameters of ionic liquids, which describe key solvent properties such as polarity and hydrogen bonding ability. Ionic liquids are represented as molecular graphs, and Graph Neural Networks are used to learn structural embeddings, combined with quantum chemical descriptors, to build predictive and interpretable models.…
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Your Own Idea!
Do you have a specific project in mind you want to investigate, that is not already listed on the thesis page? Then you can contact Prof. Nils Kriege to propose your own project idea! Your project idea must fulfill the following requirements: Requirements: Strong interest in graph learning. Contact: Nils Kriege
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Common Subgraph Problems in Tree-Like Graphs
For two given graphs, G and H, the maximum common subgraph problem (MCS) asks for the largest graph contained in both G and H. An important application occurs in cheminformatics, where the similarity of molecular graphs needs to be quantified. Unfortunately, MCS is NP-hard unless additional constraints regarding the properties of G, H, and the…
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The Complexity of Computing the Graph Edit Distance
The graph edit distance (GED) quantifies the dissimilarity of two graphs as the minimum cost of a sequence of edit operations turning one graph into the other. The problem complexity depends on the choice of the edit operation costs and the properties of the considered graphs. It has been shown that the classical NP-hard subgraph…