NetworkX
Django
Our great sponsors
NetworkX | Django | |
---|---|---|
61 | 484 | |
14,178 | 76,672 | |
1.4% | 1.1% | |
9.6 | 9.8 | |
about 6 hours ago | 6 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
NetworkX
-
Routes to LANL from 186 sites on the Internet
Built from this data... https://github.com/networkx/networkx/blob/main/examples/grap...
-
The Hunt for the Missing Data Type
I think one of the elements that author is missing here is that graphs are sparse matrices, and thus can be expressed with Linear Algebra. They mention adjacency matrices, but not sparse adjacency matrices, or incidence matrices (which can express muti and hypergraphs).
Linear Algebra is how almost all academic graph theory is expressed, and large chunks of machine learning and AI research are expressed in this language as well. There was recent thread here about PageRank and how it's really an eigenvector problem over a matrix, and the reality is, all graphs are matrices, they're typically sparse ones.
One question you might ask is, why would I do this? Why not just write my graph algorithms as a function that traverses nodes and edges? And one of the big answers is, parallelism. How are you going to do it? Fork a thread at each edge? Use a thread pool? What if you want to do it on CUDA too? Now you have many problems. How do you know how to efficiently schedule work? By treating graph traversal as a matrix multiplication, you just say Ax = b, and let the library figure it out on the specific hardware you want to target.
Here for example is a recent question on the NetworkX repo for how to find the boundary of a triangular mesh, it's one single line of GraphBLAS if you consider the graph as a matrix:
https://github.com/networkx/networkx/discussions/7326
This brings a very powerful language to the table, Linear Algebra. A language spoken by every scientist, engineer, mathematician and researcher on the planet. By treating graphs like matrices graph algorithms become expressible as mathematical formulas. For example, neural networks are graphs of adjacent layers, and the operation used to traverse from layer to layer is matrix multiplication. This generalizes to all matrices.
There is a lot of very new and powerful research and development going on around sparse graphs with linear algebra in the GraphBLAS API standard, and it's best reference implementation, SuiteSparse:GraphBLAS:
https://github.com/DrTimothyAldenDavis/GraphBLAS
SuiteSparse provides a highly optimized, parallel and CPU/GPU supported sparse Matrix Multiplication. This is relevant because traversing graph edges IS matrix multiplication when you realize that graphs are matrices.
Recently NetworkX has grown the ability to have different "graph engine" backends, and one of the first to be developed uses the python-graphblas library that binds to SuiteSparse. I'm not a directly contributor to that particular work but as I understand it there has been great results.
-
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
-
Custom libraries and utility tools for challenges
If you program in Python, can use NetworkX for that. But it's probably a good idea to implement the basic algorithms yourself at least one time.
-
Google open-sources 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://pytorch-geometric.readthedocs.io/en/latest/
--
1: https://docs.arangodb.com/3.11/data-science/adapters/
2: https://github.com/arangodb/interactive_tutorials#machine-le...
-
org-roam-pygraph: Build a graph of your org-roam collection for use in Python
org-roam-ui 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.
Django
-
AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite
>As an example, AutoCodeRover successfully fixed issue #32347 of Django.
This bug was fixed three years ago in a one-line change.[0] Presumably the fix was already in the training data.
[0] https://github.com/django/django/pull/13933
-
An Introduction to Testing with Django for Python
You should not test Django's own code — it's already been tested. For example, you don't need to write a test that checks if an object is retrieved with get_object_or_404 — Django's testing suite already has that covered.
-
Django Hello, World
Django is a high-level Python web framework that prioritizes rapid development with clear, reusable code. Its batteries-included approach supplies most of what you need for complex database-driven websites without turning to external libraries and dealing with security and maintenance risks. In this tutorial, we will build a traditional "Hello, World" application while introducing you to the core concepts behind Django.
-
Where can I create a website for free (no domain needed, basic server hosting, not something like Wix)
If you want to get into Python web development then Django can be a good place to start. https://www.djangoproject.com/
-
I like this docstring from django source code
If found this:
-
No changes detected with MAKEMIGRATION command after moving to new DataBase
Django's auth and session migration files are included with Django at https://github.com/django/django/tree/b287af5dc954628d4b336aefc5027b2edceee64b/django/contrib/auth/migrations and https://github.com/django/django/tree/b287af5dc954628d4b336aefc5027b2edceee64b/django/contrib/sessions/migrations
- What should I learn
-
The DevRel Digest November 2023: DevRel You Should Know Part One and Why I Will Never, Ever Leave Developer Relations
Dawn Wages’ name came up a few times in my call for nominations, and it’s easy to see why! Dawn is a Python Community Advocate at Microsoft. She is active in the Django community with an emphasis on people of color and queer people in tech. Dawn’s impressive resume includes OSS maintainer, member of the Wagtail Core Team, DjangoCon '21, '22, '23 Sponsorship Chair, volunteer for Django Girls, and DjangoCon Africa 2021 Sponsorship Chair.
-
CodeCraze🚀 - create your own blog in Django | Part 0 | Project Setup
In this Article, we create our own blog called CodeCraze using Django, a popular web framework written in python. Django is designed to help developers to rapidly build their web applications from concept to completion in an efficient way. Its a batteries included framework which provides out of the box functionalities such as ORM, API Integration, authentication, form handling & many more...
-
Implementing Role-Based Access Control in Django
There are many models of access control, however, in this guide, we are going to focus on Role Based Access Control (RBAC) and how to implement it in Django.
What are some alternatives?
Numba - NumPy aware dynamic Python compiler using LLVM
Nest - A progressive Node.js framework for building efficient, scalable, and enterprise-grade server-side applications with TypeScript/JavaScript 🚀
Dask - Parallel computing with task scheduling
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
julia - The Julia Programming Language
Flask - The Python micro framework for building web applications.
RDKit - The official sources for the RDKit library
Masonite - The Modern And Developer Centric Python Web Framework. Be sure to read the documentation and join the Discord channel for questions: https://discord.gg/TwKeFahmPZ
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
SymPy - A computer algebra system written in pure Python
Nuxt.js - Nuxt is an intuitive and extendable way to create type-safe, performant and production-grade full-stack web apps and websites with Vue 3. [Moved to: https://github.com/nuxt/nuxt]