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 BatchPrEL 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 run BatchPrEL, use the following command:
$ ./bin/BatchPrEL -input datasets/raw/Graph_8.txt.mtx -output dataset/output/ -iter 200 -lr 0.8 -batch 128 -algo 2 -nsamples 20 -label datasets/raw/Graph_8.txt.labels -lrforlo 0.5 -iter2nd 200
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. 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.
-init <int>, any of 0 or 1, 1 - random initialization, 0 - greedy initialization.
-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.
-algo <int>, an integer among 2 and 3.
2 - for parallel layout generation using cache blocking minibatch update.
3 - for parallel layout generation using linlog mode, (0,-1)-energy model.
-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.
-h, show help message.
default: -batch 128 -iter 600 -threads MAX -algo 2 -init 0
If you have questions, please don't hesitate to ask me (Md. Khaledur Rahman) by sending email to [email protected].