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1		-A Machine Learning Approach for Node Size Approximation in Top-down Layout
	1	+Ctrl + F for Diagrams

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1		-XWiki.mka
	1	+XWiki.sdo

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1		-Top-down Layout is a technique to draw large hierarchical diagrams from the root node downwards, scaling children down to fit in the space provided by their parents. This is in contrast to bottom-up layout where children are laid out first and the parents' dimensions are determined accordingly afterwards.
	1	+A common task for evaluating the quality of a graph drawing algorithm is the time it takes to find a specific element (as well as tasks such as finding
2	2
3		-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:
	3	+the fastest route between two nodes and others). The traditional approach would be to optimize graph aesthetic criteria (such as the scale measure or the edge crossings) to improve the readability of the graph.
4	4
5		-* Using a default base size
6		-* Counting the number of children and taking the square root as a multiplication factor for the default base size
7		-* Computing the layout of only the children (look-ahead layout)
	5	+A much simpler solution would be to just have a search function inside a diagram.
8	8
9		-The main challenge is to get an approximation that gives a suitable aspect ratio (close to what will actually be required).
	7	+In our KIELER framework, one does not only have a diagram, but always the text it is synthesized from, which leaves multiple ways to implement a search function for diagrams.
10	10
11		-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.
	9	+~1. Search in diagram
12	12
13		-== Example Top-down Layout of an SCChart ==
	11	+2. Search in text and map text occurence in diagram
14	14
15		-[[image:attach:Controller_topdown_v3.png]]
	13	+3. Traditional good layout to improve readability
16	16
	15	+4. More ideas you might have!!
	16	+
17	17	= Goals =
18	18
19		-* Use the KiCoDia benchmarking tool to extract feature vectors from existing models
20		-* Train and evaluate an ML model on the extracted data sets
21		-* Integrate the model as a new node size approximator into top-down layout
	19	+* Implement search strategies for diagrams
	20	+* Evaluate the different strategies using standard graph tasks for general graphs or human made graph (on the example of SCCharts)
22	22
23	23	= Scope =
24	24
25		-Master's (Bachelor's) Thesis
	24	+Master's/Bachelor's Thesis
26	26
27	27	= Related Work/Literature =
28	28
29		-[Under Review] M. Kasperowski and R. von Hanxleden, //Top-down Layout: Effectively Utilizing Zoom for Drawings of Compound Graphs//
	28	+* TODO graph tasks
	29	+* K. Sugiyama, S. Tagawa, and M. Toda. Methods for visual understanding of hierarchical system structures. IEEE Transactions on Systems, Man and Cybernetics, 11(2):109–125, February 1981.
30	30
31		-M. Nielsen, //Neural Networks and Deep Learning//, Determination Press, 2015 ([[http:~~/~~/neuralnetworksanddeeplearning.com/index.html>>url:http://neuralnetworksanddeeplearning.com/index.html||shape="rect"]])
32		-
33		-I. Goodfellow and Y. Bengio and A. Courville, //Deep Learning//, MIT Press, 2016 ([[https:~~/~~/www.deeplearningbook.org/>>url:https://www.deeplearningbook.org/||shape="rect"]])
34		-
35	35	= Involved Languages/Technologies =
36	36
37		-* Java / Xtend, Python
38		-* KiCo
39		-* ML Frameworks (to be chosen)
	33	+* Java / Xtend in SCCharts Synthesis
	34	+* Typescript in klighd-vscode
	35	+* Sprotty
40	40
41	41	= Supervised by =
42	42
43		-Maximilian Kasperowski in cooperation with the [[Intelligent Systems>>url:https://www.ins.informatik.uni-kiel.de/en||shape="rect"]] group.
	39	+Sören Domrös
44	44
45		-mka@informatik.uni-kiel.de
	41	+sdo@informatik.uni-kiel.de

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	1	+XWiki.sdo

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