Diagrammatic Representation and Inference: 8th International by Tim Dwyer, Helen Purchase, Aidan Delaney

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By Tim Dwyer, Helen Purchase, Aidan Delaney

This booklet constitutes the refereed lawsuits of the eighth overseas convention at the idea and alertness of Diagrams, Diagrams 2014, held in Melbourne, VIC, Australia in July/August 2014. The 15 revised complete papers and nine brief papers offered including 6 posters have been rigorously reviewed and chosen from forty submissions. The papers were prepared within the following topical sections: diagram format, diagram notations, diagramming instruments, diagrams in schooling, empirical reviews and common sense and diagrams.

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Extra resources for Diagrammatic Representation and Inference: 8th International Conference, Diagrams 2014, Melbourne, VIC, Australia, July 28 – August 1, 2014. Proceedings

Example text

Similarly, Niggemann and Stein proposed to build a database that maps vectors of structural graph features, e. g. the number of nodes and the number of connected components, to the most suitable layout algorithm with respect to 18 M. Sp¨ onemann, B. Duderstadt, and R. von Hanxleden some predefined combination of aesthetic criteria [14]. These data are gathered by applying the algorithms to a set of “typical” graphs. A suitable algorithm for a given input graph is chosen by measuring its structural features and comparing them with the entries present in the database.

For the complete graph K5 , which is not planar, even the best layouts yield a result of μc (L) = 50%, suggesting that the layout is only half as good as it could be. Purchase proposed to scale the number of crossings against an upper bound κmax defined as the number that results when all pairs of edges that are not incident to the if κmax > 0 same node cross each other [2]. Her formula is μc (L) = 1 − κκ(L) max and μc (L) = 1 otherwise. ” For instance, the graph N14 used in her evaluations has 24 nodes, 36 edges, and κmax = 558.

Here, in contrast, we seek to configure arbitrary parameters of algorithms in addition to their selection. Archambault et al. combined graph clustering with layout algorithm selection in a multi-level approach [15]. The clustering process is tightly connected with the algorithm selection, since both aspects are based on topological features of the input graph. When a specific feature is found, e. g. a tree or a clique, it is extracted as a subgraph and processed with a layout algorithm that is especially suited for that feature.

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