Parallel Batch Update Algorithm for Force-Directed Graph Layout generation in Shared Memory. This tool generates layout of graphs using multicores which is generally faster than state-of-the-art algorithms. It comes with various features, e.g., it intializes layout using random technique as well as greedy technique (helps to converge faster), it provides several options like (a,r)-energy models and Edge weights, it can approximate repulsive forces by Barnes-Hut approximation and greedy technique. We assume that the input graph is undirected, connected and symmetric.
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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.
Go to BatchLayoutCode directory and follow the instructions step by step. Once you have compiled and generated a executible file, you can run BatchLayout from this directory as following:
$ ./BatchLayoutCode/bin/BatchLayout -input ./datasets/input/3elt_dual.mtx -output ./datasets/output/ -iter 600 -batch 256 -threads 32 -algo 2
For more options, check out readme file in BatchLayoutCode folder or type ./BatchLayoutCode/bin/BatchLayout from this directory for help message.
We have also made other tools (ForceAtlas2, OpenOrd, and tsNET) available here to run. Please go to othertools folder and follow instructions step by step.
Different tools takes different input format. To increase user friendly, We provide few python scripts for file conversion in fileconversion folder. User can check out corresponding readme file.
To compute aesthetic metrics (edge crossing, min-angle, edge length variation, shape gabriel), users will need to have glam tool installed which has some other system requirements. There is another repository by Felice for computing measures such as Edge Uniformity, Stress, Neighborhood Preservation, etc. Users can check that as well.
Users can check visualize.ipynb jupyter notebook for visualization. You can run jupyter notebook using following command provided that you have it installed on your machine. Then set appropriate directory for input and output files.
jupyter notebook visualize.ipynb
jupyter notebook FinalVis.ipynb
Authors would like to thank Kwon, Felice and J Dyer for making their repositories public. Those were helpful for building script for other tools and running aesthetic metrics.
If you find this repository helpful, please cite the following paper:
Md. Khaledur Rahman, M. Haque Sujon and Ariful Azad, "BatchLayout: A batch-parallel force-directed graph layout algorithm in shared memory", The 13th IEEE Pacific Visualization Symposium (PacificVis 2020), April, 2020, Tianjin, China.
@inproceedings{rahman2020batchlayout,
title={BatchLayout: A Batch-Parallel Force-Directed Graph Layout Algorithm in Shared Memory},
author={Rahman, Md Khaledur and Sujon, Majedul Haque and Azad, Ariful},
booktitle={2020 IEEE Pacific Visualization Symposium (PacificVis)},
pages={16--25},
year={2020},
organization={IEEE}
}
If you have questions or comments, please don't hesitate to ask me (Md. Khaledur Rahman) by sending email to morahma@iu.edu.