pynetviewer: A Python repository from alanvalejo

References

[1] Valejo, Alan and Goes, F. and Romanetto, L. M. and Oliveira, Maria C. F. and Lopes, A. A., A benchmarking tool for the generation of bipartite network models with overlapping communities, in Knowledge and information systems, vol. 62, p. 1641-1669, 2019, doi: https://doi.org/10.1007/s10115-019-01411-9

PyNetViewer: A tool for visualization of bipartite, k-partite and heterogeneous networks

About

PyNetViewer is a python and igraph based tool for visualization of bipartite, k-partite and heterogeneous networks. The main aim of the PyNetViewer is the visualization of the benchmark networks synthesized by the Bnoc tool.

Usage

PyNetViewer may operate in two modes: 1. using explicit command line parameters (or options) or 2. using a JSON config file (JavaScript Object Notation).

Command line parameters

$ python pynetviewer.py [options]

Option	Domain	Default	Description
-in --input	str [FILE]	'input'	input filename '.ncol' format
-dir --output_directory	str [DIR]	'.'	directory of output file
-out --output	str [FILE]	'out'	output filename
-cnf --conf	str [FILE]	None	Input parameters in .json format
-type --file_type	str [FILE]	None	graph type file name
-m --file_membership	str [FILE]	None	membership labels file name
-w --file_weight	str [FILE]	None	vertex weight filen name
-xy --file_layout	str [FILE]	None	layout xy file name
-clr --file_color	str [FILE]	None	colors file name
-di, --delete_vertex_by_degree_le	int array	false	delete vertex by degree
-dwle, --delete_edge_by_weight_le	boolean	false	delete edge by weight less than or equal to
-cdt, --community_detection_algorithm	str	fastgreedy	community detection algorithm
-os, --overlapping_shape	str	rectangle	overlap shape
-op, --overlapping_paint	boolean	false	paint overlap vertex
-oc, --overlapping_color	str	red	overlapping vertex color
-vfc, --vertex_frame_color	str	white	vertex frame color (vertex border)
-vfw, --vertex_frame_width	float	1.0	vertex frame width (vertex border)
-v, --vertices	int array	[10, 10]	number of vertices for each layer
-k, --number_of_communites	int	2	number of communities
-mg, --margin	int	20	image margin
-vmin, --vertex_size_min	int	6	minimum vertex size
-vmax, --vertex_size_max	int	100	maximum vertex size
-wmin, --edge_weight_min	int	10	minimum vertex weight
-wmax, --edge_weight_max	int	10	maximum vertex weight
-omin, --edge_opacity_min	float	0.01	minimum opacity for degree
-omax, --edge_opacity_max	int	0.08	maximum opacity for degree
-b, --bbox	int array	[300, 300]	the bounding box of the plot
-lyt, --layout_name	str	fr	layout name
-crv, --edge_curved	boolean	false	edge curved
-rtt, --pdf_rotete	boolean	false	rotated output
-trm, --img_trim	boolean	false	trim output
-pdf, --save_pdf	boolean	false	save pdf
-png, --save_png	boolean	false	save png
-shw, --show	boolean	false	plot output
-sp --split_projections	int array	false	split the networks into its one-mode projections
-ube --use_boundary_edges	boolean	false	force the use of boundary edges in the layout
-lytn --layout_niter	int	500	number of iterations in layout
-lytg --layout_gravity	float	10.0	gravity in Force Atlas 2 layout
-lytsr --layout_scaling_ratio	float	5.0	scaling ration in Force Atlas 2 layout
-lytr --layout_to_radial	boolean	false	convert layout to radial base
-lytha --layout_hub_attraction	boolean	false	hub attraction in Force Atlas 2 layout
-giant --only_giant_component	boolean	false	plot only giant component
-vsb --vertex_size_by	str	degree	set vertex size based on degree, weight or neither of them
-vcb --vertex_color_by	str	degree	set vertex size based on degree, membership or neither of them

JSON option

$ python bnoc.py -cnf options.json

JSON format: Data is in name/value pairs, separated by commas, curly braces hold objects and square brackets hold arrays.

{
    "option": "value"
}

Examples

You can use a config file (.json) to specify the parameters, for instance:

$ python pynetviewer.py -cnf input/bipartite-1-layout-1.json

$ python pynetviewer.py -cnf input/bipartite-1-layout-2.json

$ python pynetviewer.py -cnf input/bipartite-2.json

$ python pynetviewer.py -cnf input/bipartite-3.json

$ python pynetviewer.py -cnf input/kpartite.json

$ python pynetviewer.py -cnf input/heterogeneous.json

Notes

In k-partite networks there are two types of community definitions: First, one-to-one correspondence, that is the same as the definition of community structure in unipartite networks, i.e., there are the same number of communities in the each layer and the communities are correspondents between layers (Figure 1a). 2. many-to-many correspondence, in which there are different number of communities in the layers and the communities are independents between layers (Figure 1b). The membership file must represent both scenarios, i.e., vertices in the same community must have the same community_id and vertices from different communities must have different community_id, regardless of the layer they are assigned in.

1a) One-to-one definition	1b) Many-to-many definition

Instal

Pip

$ pip install -r requirements.txt

Or Anaconda env

$ conda env create -f environment.yml
$ conda activate pynetviewer

Or Anaconda create

$ conda create --name pynetviewer python=3.7.2
$ conda activate pynetviewer
$ conda install -c anaconda numpy
$ conda install -c conda-forge python-igraph
$ conda install -c conda-forge colour
$ conda install -c anaconda pyyaml
$ conda install -c conda-forge pypdf2
$ conda install -c anaconda scipy
$ conda install -c anaconda networkx
$ sudo apt install texlive-extra-utils
$ sudo apt install imagemagick
$ pip install --upgrade bezier[full] ????

# Use python-igraph=0.7.1 for old layout versions
# If libicui18n.so.58 problem occor use
# $ conda update --all
# If libiconv.so.2 problem occor use
# $ conda install -c r libiconv

Install ForceAtlas2 for Python: https://github.com/bhargavchippada/forceatlas2

$ pip install fa2

Release History

0.1.0
- The first proper release
0.0.1
- Work in progress

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.

Fork it (https://github.com/alanvalejo/pynetviewer/fork)
Create your feature branch (git checkout -b feature/fooBar)
Commit your changes (git commit -am 'Add some fooBar')
Push to the branch (git push origin feature/fooBar)
Create a new Pull Request

Known Bugs

Please contact the author for problems and bug report.

Contact

Alan Valejo
alanvalejo@gmail.com
www.alanvalejo.com.br
Postdoctoral research fellow at the University of São Paulo (USP), Brazil

License and credits

Giving credit to the author by citing the papers [1]
The GNU General Public License v3.0
This program comes with ABSOLUTELY NO WARRANTY. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU.
Owner or contributors are not liable for any direct, indirect, incidental, special, exemplary, or consequential damages, (such as loss of data or profits, and others) arising in any way out of the use of this software, even if advised of the possibility of such damage.
This program is free software and distributed in the hope that it will be useful: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/. To-do list
Explicitly seed a global variable or parameter to achieve reproducibility
Improve usage section

alanvalejo/pynetviewer

PyNetViewer: A tool for visualization of bipartite, k-partite and heterogeneous networks