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NetworkX | orange | |
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61 | 24 | |
14,070 | 4,563 | |
1.6% | 1.9% | |
9.6 | 9.6 | |
1 day ago | 7 days ago | |
Python | Python | |
GNU General Public License v3.0 or later |
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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
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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 β 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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-π- 2022 Day 12 Solutions -π-
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!
orange
- Ask HN: What Underrated Open Source Project Deserves More Recognition?
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What exactly is AutoGPT?
Both tools are ripoffs of a data mining framework named Orange 3
- Has anybody used Orange?
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Book or web book recommendation request: a data visualization cookbook using Python for scientists.
Have you tried Orange? https://orangedatamining.com/ This is not a direct answer to your question but Orange has Python based stuff for data mining and visualization. It is very intuitive as for being a graphical interface.
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What are strictly data analysis jobs?
Or that you enter into counseling, accreditation: there already are processes somewhat working, and your expertise in (statistical) design of experiments (example entry on CRAN, a blog post) recommends a set of experiments. Your clients perform then the experiments in the lab, and you analyze the data collected. Eventually, the yield of product X is increased, with lower consumption of energy in a shorter time. You can complement R, or Python for this (there is an 101 on learnxinyminutes, too), of course with GUI programs you know and like (e.g., JMP, minitab; orange etc). There are some closer related to chemistry (e.g., DataWarrior.
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Show HN: Open-Source No-Code Platform for Machine Learning and Data Science
Honestly, I think ML should always involve at least a little bit of coding, which would be more practical. That said, this looks reasonable, good playground for experiment.
A good similar product is Orange: https://orangedatamining.com/
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Resources for data visualization (free & paid) for scientific publications
Actually....I thought of an interesting free option. Check out orange3. https://orangedatamining.com/
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Excel Alternatives?
I love to play with my dataset using OrangeDatamining. Very easy to use. Docs and example available. Itβs like Data Modeler from IBM but better bcos it is open project :-)
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[D] Why Hasn't FOSS Drag-and-Drop ML tools taken off yet?
Currently, I am looking around for modules for Knime and Orange and looked at some of the modules, and realized that it does not have enough tools within their tool kit (e.g. text data analysis, network analysis, image classification).
What are some alternatives?
glue - Linked Data Visualizations Across Multiple Files
Numba - NumPy aware dynamic Python compiler using LLVM
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
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
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.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows