TheAlgorithms
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TheAlgorithms | NetworkX | |
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61 | 61 | |
176,856 | 14,070 | |
1.0% | 1.6% | |
9.7 | 9.6 | |
8 days ago | about 22 hours ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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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.
TheAlgorithms
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I am studying my college Python so can I learn algorithms from it?
The Algorithms Contains many open source implementations of algorithms. Check it out.
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Any tips to improve my coding abilites ?
There is no one way to learn all these but here are some resources: 1. Gooking algorithms [https://edu.anarcho-copy.org/Algorithm/grokking-algorithms-illustrated-programmers-curious.pdf\] 2. Algorithms in all languages [https://the-algorithms.com/] 3. Node js best practices. [https://github.com/goldbergyoni/nodebestpractices] 4. Refactoring [https://refactoring.guru/] 5. Learn about Clean Code and Clean Architecture from uncle bob. https://www.youtube.com/watch?v=NeXQEJNWO5w&ab_channel=StreamAConStreamingConferences
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Self taught developers: where are you in your journey?
DSA basics
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A site that hosts implementations of various programming algorithms in different languages
There's also The Algorithms. Many implementations are unfortunately low quality. The Lua ones (disclaimer: I wrote them) should be fine however.
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How worried are you about AI taking over music?
Python 940 contributors 152k stars
- Cool Github repositories for Everyone
- How can I improve my problem solving and algorithm skills?
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Open Source Repositories
Python - 146k
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git clone error
You could try https://github.com/TheAlgorithms/Python or https://gitlab.com/gnutls/gnutls to check if it's a problem with the server side or generally something on your connection.
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Ask HN: I like studying the concept of abstractions
** meta-algorithms site : https://the-algorithms.com
NetworkX
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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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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.
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Network Graph Layer3 Topology
I had some success using Networkx in the past: https://networkx.org/
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PageRank Algorithm for Graph Databases
Common graph databases are network-based for scaling purposes. Sqlite is a in-file 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/
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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
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-🎄- 2022 Day 12 Solutions -🎄-
Sure! I didn't actually use any path-finding 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 are some alternatives?
Numba - NumPy aware dynamic Python compiler using LLVM
Dask - Parallel computing with task scheduling
julia - The Julia Programming Language
RDKit - The official sources for the RDKit library
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
SymPy - A computer algebra system written in pure Python
python-ds - No non-sense and no BS repo for how data structure code should be in Python - simple and elegant.
new-world-fishing-bot - user friendly python script who is able to catch fish in the game New World
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
networkit - NetworKit is a growing open-source toolkit for large-scale network analysis.
python-patterns - A collection of design patterns/idioms in Python
algorithms