visla

Easy and attractive visualizations of sparse matrices and graphs.

Samples

Usage

visla is a command line program to visualize graphs in the style of Yifan Hu's Gallery of Large Graphs.

The two figures above were generated with

visla grid1_dual.mtx
visla M10PI_n.mtx

visla accepts a variety of input formats including: Matrix Market, CSV, npz (saved by scipy.sparse.save_npz), dot/gv. You can try it out on the small matrices in matrices/.

If you want more fine grained control (like a custom colormap) use the visla.VGraph class (documentation below) in a Python program.

Installation

Once you've obtained the source code you can install it with pip

cd visla
pip install .

Documentation

The visla command line program has a help message; the rest of this section is dedicated to highlighting the visla.VGraph class. The user is encouraged to read the source code as necessary.

visla is powered by an extension of PyGraphviz's AGraph class. Specifically, the following methods and fields have been added:

• from_file(): read in graph from a file, currently supporting:
  • mtx: Matrix Market
  • csv: comma separated values (first line dimensions, rest of lines i, j, A_ij)
  • npz: npz files saved by scipy.sparse.save_npz
  • gv/dot: Graphviz dot language files
• visualize(): visualize a laid out graph; creates, shows, and destroys its own figure by default, but may be called as visualize(fig,ax) if you want control of the figure (for example to use savefig())
• bg_color: color of background
• cm: color map taking edge length -> edge color
• bipartite: visualize the bipartite graph corresponding to the sparse matrix (useful for non-square matrices)

See bin/visla to see how some of these options are used in practice.

Notes

• I suggest using some sort of "SuiteSparse-getter" like ssgetpy (available via pip) to retrieve .mtx files before feeding them to visla.

• A few .mtx files are included: will57.mtx is small and good for prototyping, grid1_dual.mtx and M10PI_n.mtx are used to generate the figures above.

• Graphs with about 40000 nodes and 130000 edges can be visualized in under 2 minutes on a laptop.

Sources

The pipeline to create these visualization was informed by the following:

• Yifan Hu's Gallery of Large Graphs
• Tim Davis's synopsis of how Yifan Hu generated his figures

Some interesting facts learned along the way:

• edges are colored according to their lengths
• these visualizations are only in two dimensions

Limitations

• Currently all connected components are visualized, but ideally one could (optionally) visualize only the largest component.
• The parser to get connectivity and coordinates was made to work on practical examples. I don't know of any cases where it fails, but be wary.