No learning curve - your code stays the same, function names are the same, all from the comfort of Python.
Import your data once and update incrementally it without having to reload your entire dataset.
Run multiple algorithms without having to upload your data every single time.
Use Cypher in addition to Python for ultimate flexibility and complex analysis.
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.
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 which you can query from the comfort of Python by using your well known NetworkX functions. (P.s. Your code stays the same! )
All you have to do, is use the nx_to_cypher function from GQLAlchemy, and the rest takes case of itself. The function does all the heavy lifting of transferring the NetworkX structured data into Memgraph. That's it!
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):