Graphs in Artificial Intelligence
Neural Networks


All positions have been filled, if you are interested in working with us, contact , we might apply for funding together.

Student work

  1. Project work on the topic "Graph Type Transformations"

    • Creation of a Python package for transformations from one to another graph type (based on the paper: https://arxiv.org/pdf/2109.10708.pdf)
    • Extension of the algorithms (in algorithm type or optimized versions)

  2. For more information on this project, see theproject description.

  3. Seminar on the topic "Graph Neural Networks Decoder using Stochastic Processes (SP)"

    • Creation of an overview of similarities, differences, advantages and disadvantages of different SP which are used in Graph Neural Networks (GNNs).

  4. For more information on this seminar, see theseminar description.

  5. Seminar or project on the topic "Explainable Graph Neural Networks"

    • Create an overview of existing methods of explainability for GNN algorithms in- cluding their similarities, differences, adventages and disadventages.

  6. For more information on this seminar, see thedescription


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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