speed-comparison
NetworkX
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speed-comparison | NetworkX | |
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9 | 61 | |
422 | 14,178 | |
- | 1.6% | |
4.6 | 9.6 | |
2 months ago | 3 days ago | |
Earthly | Python | |
MIT License | 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.
speed-comparison
- Douglas Crockford: “We should stop using JavaScript”
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How often do you guys actually use C?
For example, Java runs on the JVM (Java Virtual Machine) instead of running directly on the hardware, and it also has a garbage collector to handle memory management. Running on a virtual machine means your code is more abstracted: you only have to worry about the JVM and not about the platform you’re running on (since the JVM is the platform), and it’s more portable since your code can go on anything that runs the JVM. But running the JVM as an intermediate layer takes more computing power and so does running garbage collection, meaning that you experience a performance penalty. Here’s one benchmark I could find comparing the use of different programming languages to compute pi, in which Java took about 3x as long as C to complete the same task
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AITA for telling my 9 y/o daughter she sucked for not writing professional-level code?
Or you've got the speed comparisons (https://github.com/niklas-heer/speed-comparison) -- Python is probably something like 10% the speed of C/C++ (although, like I said, 99% of the time that's comparable to premature optimization).
- sou iniciante e com uma dúvida, python é realmente lento? ou é só meme?
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Why does Julia use jit?
Looks like a PR was merged yesterday to make the code more simd friendly https://github.com/niklas-heer/speed-comparison/pull/52
- speed comparison of various programming languages, Julia (AOT) is on fire!!!
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An Apple fan walks into a bar....
Sure, they could have chosen Python. But I doubt the language differences account for even a noticeable percentage of the slowness of Brew.
- There is framework for everything.
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?
arl - lists of most popular repositories for most favoured programming languages (according to StackOverflow)
Numba - NumPy aware dynamic Python compiler using LLVM
OpenCV - Open Source Computer Vision Library
Dask - Parallel computing with task scheduling
docx4j - JAXB-based Java library for Word docx, Powerpoint pptx, and Excel xlsx files
julia - The Julia Programming Language
pivotnacci - A tool to make socks connections through HTTP agents
RDKit - The official sources for the RDKit library
Apache ZooKeeper - Apache ZooKeeper
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
NumPy - The fundamental package for scientific computing with Python.
SymPy - A computer algebra system written in pure Python