Iterative Approximate Analysis Of Graph-Structured Data For Precision Medicine

This project aims to develop novel algorithms for the maximum weighted clique (MWC) problem, which appears in various data analysis pipelines in precision medicine. The MWC problem is NP-hard in nature, which makes it particularly challenging given the exponentially increasing amount of data it is applied to.

Although several attempts have been made to solve the maximum weighted clique problem in large graphs, there is still much opportunity for lowering the execution time necessary to find a satisfactory solution. In this project in particular we are investigating approximate algorithms for the MWC problem. We are working towards an algorithm that achieves a very high quality solution (i.e., finding a clique with weight very close to the MWC) in polynomial time.

Project Members

IBM will provide industrially relevant context on knowledge extraction from graph-structured data. They have extensive experience in this area by building scalable software systems for the analysis of massive-scale graph data. They will moreover provide access to relevant datasets.


This PhD project is funded by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Actions.