Bit
NetworkX
Bit | NetworkX | |
---|---|---|
69 | 61 | |
17,588 | 14,200 | |
0.5% | 0.9% | |
9.9 | 9.6 | |
4 days ago | 5 days ago | |
TypeScript | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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.
Bit
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Theming using CSS Variables? Turn Them into VS Code Snippets for Faster, Error-Free Coding
Our demo solution was built using Bit, which allows us to create shareable components, render component “previews,” generate component docs, and so on.
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UI Libraries are Dying: What’s Next?
UI libraries come with their own set of challenges, which greatly limit their effectiveness. These challenges stem from more fundamental problems related to code sharing and reuse. Let’s explore some of these challenges and examine how a new entity, the Bit component, addresses them.
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Composable Software Architectures are Trending: Here’s Why
The following diagram showcases how bit shows the dependency graph of modified components and their dependents.
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Micro Frontends with Vite and Bit
This tutorial demonstrates how to build a micro frontend application using Vite and Bit.
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Implementing a Service Oriented Architecture in 2024
Bit: A next-generation build system for composable software.
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3 Principles for Component-Driven Development
Bit drives a paradigm shift in the way we structure our software and collaborate on code. Its component-based approach produces more maintainable projects and more effective collaboration. However, the power of Bit is best harnessed when you adopt a certain mindset. This blog aims to guide you through the core principles and methodologies of building software with independent Bit components.
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How To Build a Node.js Express App in Under 5 Minutes?
And one such tool that I've found that supports this component-driven approach is Bit.
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Monorepo, Poly-repo, or No Repo at all?
This blog will explain how Bit can be used to implement any architecture and transform “fatal” decisions that seem too hard to change into decisions that are easy to make and change.
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React monorepo with open-source apps and proprietary libs
Oh can I address theses issues. I already looked at tools like Nx or Bit, but they aren't matching our needs with closed source libs.
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How to Build and Publish Your First React NPM Package
To begin, you need to prepare your environment. A few ways to build a React package include tools like Bit, Storybook, Lerna, and TSDX. However, for this tutorial, you will use a zero-configuration bundler for tiny modules called Microbundle.
NetworkX
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Routes to LANL from 186 sites on the Internet
Built from this data... https://github.com/networkx/networkx/blob/main/examples/grap...
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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.
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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
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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.
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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/
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1: https://docs.arangodb.com/3.11/data-science/adapters/
2: https://github.com/arangodb/interactive_tutorials#machine-le...
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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.
What are some alternatives?
single-spa - The router for easy microfrontends
Numba - NumPy aware dynamic Python compiler using LLVM
storybook - Storybook is a frontend workshop for building UI components and pages in isolation. Made for UI development, testing, and documentation.
Dask - Parallel computing with task scheduling
nx - Smart Monorepos · Fast CI
julia - The Julia Programming Language
Commander.js - node.js command-line interfaces made easy
RDKit - The official sources for the RDKit library
piral - Framework for next generation web apps using micro frontends. :rocket:
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
lit-element - LEGACY REPO. This repository is for maintenance of the legacy LitElement library. The LitElement base class is now part of the Lit library, which is developed in the lit monorepo.
SymPy - A computer algebra system written in pure Python