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).

Because graphs are complex feature vectors and the solution space is very large without necessarily one correct and optimal answer a ML-based approach may help find good solutions.

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

Goals

• use 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

Example Top-down Layout of an SCChart

Related Work/Literature

[WIP] Top-down layout paper

http://neuralnetworksanddeeplearning.com/index.html

https://www.deeplearningbook.org/

Supervised by

Maximilian Kasperowski