Get your NetworkX application production-ready ⚡

NetworkX is a powerful library and we love it! Learn how you can combine NetworkX and Memgraph to speed up the development process and make your queries more powerful and amazingly fast!

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Buff Shiba with Memgraph & NetworkX tattoos

NetworkX + Memgraph = 🚄


Slow imports?
Import your data once and update incrementally it without having to reload your entire dataset.


Multiple algorithms?
Run multiple algorithms without having to upload your data every single time.


Love Cypher?
Use Cypher in addition to Python for ultimate flexibility and complex analysis.

Incremental changes?
Serve real-time query results to your user-facing applications.


Millions of nodes?
Handle large graphs with millions of nodes & edges by leveraging Memgraph's C/C++ implementation of some of the most popular NetworkX algorithms including Cycle Detection, and PageRank.

Get Started in a few seconds

pip install gqlalchemy

import networkx as nx
from gqlalchemy import nx_to_cypher, Memgraph

graph = nx.Graph()
db = Memgraph()
for query in nx_to_cypher(graph):    

How it works under the hood

By transferring your graph representation from the hard disk into memory and keeping it there you cut down the re-import overhead. The data gets persisted in memory in a Graph database.You query the database from the comfort of Python by using your well known NetworkX functions. (your code stays the same 😄 )

To accomplish the above we've made it simple by using GQLAlchemy - and extended it with a function nx_to_cypher. That function does all the heavy lifting of transferring the NetworkX structured data into Memgraph (adapting the structure as needed so you don't have to worry about that).

No learning curve - your code stays the same, function names are the same, all from the comfort of Python.

By joining you will get access to all things NetworkX. Demo apps, tutorials, best practices and you will join a community of builders around NetworkX.