grand VS netgraph

Cypher is a super useful language for querying graph structures, but sometimes it's overkill to load a tiny graph into Neo4j or memgraph. We wrote this tool to act as an abstraction layer so you can query in-memory graph data -- or, using [Grand](https://github.com/aplbrain/grand), rewrite Cypher queries to run on SQLite dbs or even other graph databases that don't support Cypher out of the box. Hoping it'll be helpful to those in the network theory, graph ML, and data science communities!

Hey cool! I'm super curious to hear more about this. I _also_ wrote a pseudo-graph-database on DynamoDB (https://github.com/aplbrain/grand) :) It pretends it's a networkx.Graph, generally, but we also have a Cypher implementation on top of it.

Would love to chat more about this sometime if you were interested!

You may like my Netgraph library [1], which is a Python library that aims to complement networkx, igraph, and graph-tool with publication-quality visualisations.

Netgraph implements numerous node layout algorithms and several edge routing routines. Uniquely among Python alternatives, it handles networks with multiple components gracefully (which otherwise break most node layout routines), and it post-processes the output of the node layout and edge routing algorithms with several heuristics to increase the interpretability of the visualisation (reduction of overlaps between nodes, edges, and labels; edge crossing minimisation and edge unbundling where applicable). The highly customisable plots are created using Matplotlib, and the resulting Matplotlib objects are exposed in an easily queryable format such that they can be further manipulated and/or animated using standard Matplotlib syntax. Finally, Netgraph also supports interactive changes: with the InteractiveGraph class, nodes and edges can be positioned using the mouse, and the EditableGraph class additionally supports insertion and deletion of nodes and edges as well as their (re-)labelling through standard text-entry.

[1] https://github.com/paulbrodersen/netgraph

However, last weekend, there was a huge spike in downloads: instead of the 0-5 downloads that are typical for a normal weekend day, there were 2000 downloads, both on Saturday and Sunday [2]. I would love to know what happened here, or at least, I would like to be able to find out the next time something like this happens. Obviously, unlike a normal business, I don't control the distribution, so I can't measure the traffic with Google Analytics or similar tools.

I would love to hear how other people that have open source projects are getting their intel into their user base.

[1] https://github.com/paulbrodersen/netgraph

[2] https://pepy.tech/project/netgraph