Metadata-Version: 2.1
Name: squashy
Version: 0.1.1
Summary: Large scale graph compression and summarization tool built on Memgraph.
Home-page: https://github.com/Minyall/squashy
Author: James Allen-Robertson
Author-email: minyall@gmail.com
Requires-Python: >=3.10,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: mini-memgraph (>=0.1.0,<0.2.0)
Requires-Dist: pandas (>=2.0.0,<3.0.0)
Requires-Dist: plotly (>=5.14.1,<6.0.0)
Requires-Dist: tqdm (>=4.65.0,<5.0.0)
Project-URL: Repository, https://github.com/Minyall/squashy
Description-Content-Type: text/markdown

# ➡️Squashy⬅️

Large scale graph compression and summarization tool for research and analysis.

### Note on suitability for use
➡️Squashy⬅️ is relatively new. It was developed for one of my own academic research projects. The principles behind it are based on published research, however the implementation is my own. I think it works well, however it could do with more testing

## What does it do?

At some point the key tools of network analysis struggle with scale. Beyond a few hundred nodes, graphs become too dense
to visualise. Nodes too numerous to detect communities.

This can become even more of an issue in a research context, where you often want to run analysis multiple times, tweak
settings or actually _see_ your data.

➡️Squashy⬅️can compress graphs made up of millions of nodes and edges into a graph of a few hundred nodes that retains the overall structure.

## How does it work?


