Wiki source code of Machine Learning Approach for Node Size Approximation in Top-down Layout
Version 2.1 by Maximilian Kasperowski on 2022/08/26 08:10
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1.1 | 1 | 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: |
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| 3 | * using a default base size | ||
| 4 | * counting the number of children and taking the square root as a multiplication factor for the default base size | ||
| 5 | * computing the layout of only the children (look-ahead layout) | ||
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| 7 | The main challenge is to get an approximation that gives a suitable aspect ratio (close to what will actually be required). | ||
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| 9 | 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. | ||
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| 11 | This topic will be supervised in cooperation with the [[Intelligent Systems>>url:https://www.ins.informatik.uni-kiel.de/en||shape="rect"]] group. | ||
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| 13 | = Goals = | ||
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| 15 | * use kicodia benchmarking tool to extract feature vectors from existing models | ||
| 16 | * train and evaluate an ML model on the extracted data sets | ||
| 17 | * integrate the model as a new node size approximator into top-down layout | ||
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| 19 | == Example Top-down Layout of an SCChart == | ||
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| 21 | [[image:attach:Controller_topdown_v3.png]] | ||
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| 23 | = Related Work/Literature = | ||
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| 25 | [WIP] Top-down layout paper | ||
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| 27 | [[http:~~/~~/neuralnetworksanddeeplearning.com/index.html>>url:http://neuralnetworksanddeeplearning.com/index.html||shape="rect"]] | ||
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| 29 | [[https:~~/~~/www.deeplearningbook.org/>>url:https://www.deeplearningbook.org/||shape="rect"]] |