Graphs in Artificial Intelligence
Neural Networks


The GAIN group investigates the application of Deep Learning techniques on graphs to address various challenges in the field of Graph Learning. The focus is on the study of dynamic graphs due to their high degree of freedom in modeling tasks.

The research includes different topics such as:

  • the development of Deep Learning models applicable for dynamic graphs respecting the variety of existing graph types and the associated difficulties,
  • the consideration of different dynamics w.r.t. the graph structure and attributes and consequent adaptations of established approaches,
  • the installation of an appropriate explainability mechanism for the developed models to achieve an applicability to real-world problems,
  • the development of GNNs which enhance the efficiency and safety of power networks by directly making use of their topology

In cooperation withFraunhofer IEE, problems from supply networks (electric power transmission) serve as future applications of the algorithms arising from the GAIN project.


We are funded by the Federal Ministry of Education and Research Germany (BMBF), under the funding code 01IS20047A, according to the 'Policy for the funding of female junior researchers in Artificial Intelligence'.

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