NetworkX
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NetworkX | graph | |
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61 | 33 | |
14,256 | 1,736 | |
1.3% | - | |
9.6 | 5.3 | |
6 days ago | 26 days ago | |
Python | Go | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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
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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.
graph
- Create, analyze, and modify graphs and networks in Go
- Show HN: Creating, Modifying, Analyzing, and Visualizing Graphs/Networks in Go
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Digger is trending on GitHub in Golang
Awesome project, and nice to see that it seems to use my graph library (graph) for managing dependencies!
- graph: A library for creating generic graph data structures and modifying, analyzing, and visualizing them
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Dagger V3 Release (generic/concurrency-safe Directed Acyclic Graph)
Why should I use yours over this?
- graph v0.20 adds support for adding vertices and edges from other graphs, retrieving and updating edges, computing spanning trees, and more
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GitHub doesn't show the latest commit and rejects any pushes
Any ideas how I could've ended up with this and how I could resolve this? The repository is github.com/dominikbraun/graph.
- graph v0.16 supports integrating any storage backend for storing graph data structures
- Visualize complex networks and structures in Go
What are some alternatives?
Numba - NumPy aware dynamic Python compiler using LLVM
btree - BTree provides a simple, ordered, in-memory data structure for Go programs.
Dask - Parallel computing with task scheduling
scan - Scan provides the ability to to scan sql rows directly to any defined structure.
julia - The Julia Programming Language
be - The Go test helper for minimalists
RDKit - The official sources for the RDKit library
bob - SQL query builder and ORM/Factory generator for Go with support for PostgreSQL, MySQL and SQLite
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
warg - Declarative and Intuitive Command Line Apps with Go
SymPy - A computer algebra system written in pure Python
JGraphT - Master repository for the JGraphT project