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NetworkX Alternatives
Similar projects and alternatives to NetworkX



InfluxDB
Power RealTime Data Analytics at Scale. Get realtime insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in realtime with unbounded cardinality.



snap
Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.

Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more


WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easytouse, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

Interactive Parallel Computing with IPython
IPython Parallel: Interactive Parallel Computing in Python


networkit
NetworKit is a growing opensource toolkit for largescale network analysis.





graph
A library for creating generic graph data structures and modifying, analyzing, and visualizing them.

mintable
🍃 Automate your personal finances – for free, with no ads, and no data collection.



pythongraphblas
Python library for GraphBLAS: highperformance sparse linear algebra for scalable graph analytics

SaaSHub
SaaSHub  Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NetworkX reviews and mentions

Build the dependency graph of your BigQuery pipelines at no cost: a Python implementation
In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery.
 NetworkX – Network Analysis in Python

Google opensources their graph mining library
For those wanting to play with graphs and ML I was browsing the arangodb docs recently and I saw that it includes integrations to various graph libraries and machine learning frameworks [1]. I also saw a few jupyter notebooks dealing with machine learning from graphs [2].
Integrations include:
* NetworkX  https://networkx.org/
* DeepGraphLibrary  https://www.dgl.ai/
* cuGraph (Rapids.ai Graph)  https://docs.rapids.ai/api/cugraph/stable/
* PyG (PyTorch Geometric)  https://pytorchgeometric.readthedocs.io/en/latest/

1: https://docs.arangodb.com/3.11/datascience/adapters/
2: https://github.com/arangodb/interactive_tutorials#machinele...

orgroampygraph: Build a graph of your orgroam collection for use in Python
orgroamui is a great interactive visualization tool, but its main use is visualization. The hope of this library is that it could be part of a larger graph analysis pipeline. The demo provides an example graph visualization, but what you choose to do with the resulting graph certainly isn't limited to that. See for example networkx.

Network Graph Layer3 Topology
I had some success using Networkx in the past: https://networkx.org/

PageRank Algorithm for Graph Databases
Common graph databases are networkbased for scaling purposes. Sqlite is a infile database. So just run graph algorithms on a stringifed json stored as a text on sqlite.
See the networkx module for the common graph algorithms https://networkx.org/

NetworkX 3.0
A good place to start specifically for NetworkX would be to go through the new contributor documentation: https://networkx.org/documentation/latest/developer/new_cont...
We also have some structured projects https://networkx.org/documentation/latest/developer/projects... but they are usually for programs like GSoC/Outreachy.
Feel free to start a discussion https://github.com/networkx/networkx/discussions if you are looking for something specific :)
[I am one of the NetworkX devs]
A key feature of this release is pluggable backends incl gpu based ones which should greatly affect performance

🎄 2022 Day 12 Solutions 🎄
Sure! I didn't actually use any pathfinding algorithm  I used networkx to do the pathfinding. Essentially, I created a directed graph in networkx which allowed me to model each location as a node and then place a directed edge between them if I was allowed to move from one to the next following the rules (wasn't jumping up more than one step at a time). Once I had built the map, I used the shortest_path_length command in networkx to find the shortest path and compute its length. Let me know if this makes sense or if you want more explanation!

What is your favorite ,most underrated 3rd party python module that made your programming 10 times more easier and less code ? so we can also try that out :) .as a beginner , mine is pyinputplus
Networkx. The hardtofind but very powerfull module for working with graphs (as in: 🕸️ networks, not as in: 📈📊 graphical charts).

A note from our sponsor  WorkOS
workos.com  2 Mar 2024
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networkx/networkx is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of NetworkX is Python.