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Graphs are complex feature vectors and the solution space is very large without necessarily one correct and optimal answer. Therefore, a machine learning (ML)-based approach may help find good solutions.

This topic will be supervised in cooperation with the Intelligent Systems group.

Example Top-down Layout of an SCChart

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Goals

• Use the KiCoDia benchmarking tool to extract feature vectors from existing models
• Train and evaluate an ML model on the extracted data sets
• Integrate the model as a new node size approximator into top-down layout

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[Under Review] M. Kasperowski and R. von Hanxleden, Top-down Layout: Effectively Utilizing Zoom as the Third Dimensionfor Drawings of Compound Graphs

M. Nielsen, Neural Networks and Deep Learning, Determination Press, 2015 (http://neuralnetworksanddeeplearning.com/index.html)

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Supervised by

Maximilian Kasperowski in cooperation with the Intelligent Systems group.

mka@informatik.uni-kiel.de