TODO Satz zu top-down

In top-down layout a strategy needs to be used to set node sizes without knowledge of the hierarchical contents of the node as that has not been processed/laid out at that point. Current strategies are:

• Using a default base size
• Counting the number of children and taking the square root as a multiplication factor for the default base size
• Computing the layout of only the children (look-ahead layout)

The main challenge is to get an approximation that gives a suitable aspect ratio (close to what will actually be required).

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

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

Scope

Bachelor's/Master's Thesis

Related Work/Literature

[WIP] Top-down layout paper

http://neuralnetworksanddeeplearning.com/index.html

https://www.deeplearningbook.org/

Involved Languages/Technologies

• Java / Xtend, Python
• KiCo
• ML Frameworks (to be chosen)

Supervised by

Maximilian Kasperowski

mka@