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GLAM: Graph Layout Aesthetic Metrics

A high-performance implementation for computing graph layout aesthetic metrics described in our paper:

@article{kwon18wgl,
    title={{What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization}},
    author={Kwon, Oh-Hyun and Crnovrsanin, Tarik and Ma, Kwan-Liu},
    journal={IEEE Transactions on Visualization and Computer Graphics},
    year={2018},
    volume={24},
    number={1},
    pages={478-488}
}

Oh-Hyun Kwon, Tarik Crnovrsanin, and Kwan-Liu Ma are with VIDI Labs at the University of California, Davis.

Requirements

Build

mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release; make

Usage

# glam FILEPATH(s) -m METRIC(s)
> ./glam ../data/karate.dot -m crosslessness
Loading graph: ../data/karate.json
Computing metric: crosslessness
crosslessness=0.970909 (num_edge_crossings=72)

# multiple files and metrics
> ./glam ../data/karate.dot ../data/power.json -m crosslessness shape_gabriel
Loading graph: ../data/karate.dot
Computing metric: crosslessness
crosslessness=0.970909 (num_edge_crossings=72)
Computing metric: shape_gabriel
shape_gabriel=0.376176

Loading graph: ../data/power.json
Computing metric: crosslessness
crosslessness=0.999888 (num_edge_crossings=2426)
Computing metric: shape_gabriel
shape_gabriel=0.320775

# help
> ./glam --help
Options:
   -i [ --input-file ] arg input file(s)
   -m [ --metric ] arg     metric(s) to compute. Available metrics:
                           crosslessness, edge_length_cv, shape_gabriel,
                           shape_delaunay
   --help                  print help message

Metrics

crosslessness: This metric is defined in H. C. Purchase. Metrics for Graph Drawing Aesthetics. Journal of Visual Languages and Computing, 13(5):501–516, 2002.

> ./glam ../data/cond-mat.json -m crosslessness
Loading graph: ../data/cond-mat.json
Computing metric: crosslessness
crosslessness=0.995233 (num_edge_crossings=5396100)

edge_length_cv: This metric is defined in S. Hachul and M. Junger. Large-Graph Layout Algorithms at Work: An Experimental Study. Journal of Graph Algorithms and Applications, 11(2):345–369, 2007. The normalized definition is in our paper.

> ./glam ../data/cond-mat.json -m edge_length_cv
Loading graph: ../data/cond-mat.json
Computing metric: edge_length_cv
edge_length_cv=0.724552 (normalized_cv=0.00332122)

min_angle: This metric is defined in H. C. Purchase. Metrics for Graph Drawing Aesthetics. Journal of Visual Languages and Computing, 13(5):501–516, 2002.

> ./glam ../data/cond-mat.json -m min_angle
Loading graph: ../data/cond-mat.json
Computing metric: min_angle
min_angle=0.397181

shape_delaunay: This metric is defined in P. Eades, S.-H. Hong, A. Nguyen, and K. Klein. Shape-Based Quality Metrics for Large Graph Visualization. Journal of Graph Algorithms and Applications, 21(1):29–53, 2017.

> ./glam ../data/power.json -m shape_delaunay
Loading graph: ../data/power.json
Computing metric: shape_delaunay
shape_delaunay=0.274121

shape_gabriel: This metric is defined in P. Eades, S.-H. Hong, A. Nguyen, and K. Klein. Shape-Based Quality Metrics for Large Graph Visualization. Journal of Graph Algorithms and Applications, 21(1):29–53, 2017.

> ./glam ../data/power.json -m shape_gabriel
Loading graph: ../data/power.json
Computing metric: shape_gabriel
shape_gabriel=0.320775

Data format

See data directory for example data files. The example data are obtained from graph-tool.

Graphviz dot format:

graph G {
0 [x="-17.872", y="24.5203"];
1 [x="-13.6346", y="20.4381"];
2 [x="-16.0092", y="16.7716"];
1--0 ;
2--0 ;
}

JSON format:

{
    "nodes": [
        {"x": -17.872019430477007, "y": 24.520299748437747},
        {"x": -13.634590227372781, "y": 20.438110276467413},
        {"x": -16.009214853752148, "y": 16.771614256121076}
    ],
    "links": [
        {"source": 0, "target": 1},
        {"source": 0, "target": 2}
    ]
}

Acknowledgement

This research has been sponsored by the U.S. National Science Foundation through grant IIS-1741536: Critical Visualization Technologies for Analyzing and Understanding Big Network Data.