BatchTree generates edge-crossing-free and label-overlapping-free layout of a tree. This is a supporting scalable implementation for the paper Scalable Methods for Readable Tree Layouts.
Users need to have following softwares/tools installed in their PC. The source code was compiled and run successfully in both linux and macOS.
GCC version >= 4.9
OpenMP version >= 4.5
Python3 packages: matplotlib, scipy, networkx, numpy
Some helpful links for installation can be found at GCC, OpenMP and Environment Setup.
To compile BatchTree type the following command on terminal:
$ make clean
$ make
This will generate an executible file in bin folder.
Input file must be in matrix market format (check here for details about .mtx file). A lot of datasets can be found at suitesparse website. We provide few example input files in datasets/input directory.
To generate crossing-free layout using BatchTree, use the following command:
$ ./bin/BatchTree -input datasets/raw/Graph_8.txt.weighted.mtx -output datasets/output/ -iter 120 -lr 0.8 -batch 128 -nsamples 20 -label datasets/raw/Graph_8.txt.labels -lrforlo 0.5 -iter2nd 150
Here, -input is the full path of input file, -output is the directory where output file will be saved, -iter is the number of iterations, -batch is the size of minibatch which is 128 here, and -algo is the choice of algorithm to run which is 2 represending cache blocking stochastic minibatch update algorithm.
After running the first step, to remove existing label overlaps and increase area utilization, we can use this post-processing step. In this step, we will initialize the layout generated in previous step as follows:
$ ./bin/BatchTree -input datasets/raw/Graph_8.txt.weighted.mtx -output datasets/output/ -label datasets/raw/Graph_8.txt.labels -scalingbox 400 -box 100 -psamples 600 -expc 0.01 -initf datasets/output/Graph_8.txt.weighted.mtxBatchTree128PARAOUT120.txt
We can run this post-processing step multiple times to get the desired output.
All options are described below:
-input <string>, full path of input file (required).
-output <string>, directory where output file will be stored.
-batch <int>, size of minibatch.
-algo <int>, an option from 1, 2, or 3.
1 - edge length aware version
2 - compactness aware version
3 - general version
-nsamples <int>, number of samples to compute repulsive force.
-initf <string>, a layout file to initializee the coordinates of all vertices.
-iter <int>, number of iteration.
-lr <float>, learning rate for edgecrossing free drawing.
-threads <int>, number of threads, default value is maximum available threads in the machine.
-lrforlo <float>, learning rate for overlap removal step.
-iter2nd <int>, iterations for overlap removal step.
-scalingbox <float>, scaling box sizee for post-processing.
-box <float>, box size for post-processing.
-psamples <int>, number of samples for post-processing.
-expc <float>, expected compactness value of post-processing.
-h, show help message.
default: -batch 128 -iter 200 -threads MAX -nsamples 10
To compute the edge-length preservation measure, please run the following command:
python calcMeasuresWeighted.py datasets/raw/Graph_8.txt.weighted.mtx datasets/output/Graph_8.txt.weighted.mtxBatchTree128PARAOUT0.txt datasets/raw/Graph_8.txt.labels
If you have questions, please don't hesitate to ask me (Md. Khaledur Rahman) by sending email to [email protected].