netgraph
NetworkX
netgraph | NetworkX | |
---|---|---|
5 | 61 | |
644 | 14,200 | |
- | 0.9% | |
7.4 | 9.6 | |
3 months ago | 2 days ago | |
Python | Python | |
GNU General Public License v3.0 only | 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.
netgraph
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NetworkX – Network Analysis in Python
You may like my Netgraph library [1], which is a Python library that aims to complement networkx, igraph, and graph-tool with publication-quality visualisations.
Netgraph implements numerous node layout algorithms and several edge routing routines. Uniquely among Python alternatives, it handles networks with multiple components gracefully (which otherwise break most node layout routines), and it post-processes the output of the node layout and edge routing algorithms with several heuristics to increase the interpretability of the visualisation (reduction of overlaps between nodes, edges, and labels; edge crossing minimisation and edge unbundling where applicable). The highly customisable plots are created using Matplotlib, and the resulting Matplotlib objects are exposed in an easily queryable format such that they can be further manipulated and/or animated using standard Matplotlib syntax. Finally, Netgraph also supports interactive changes: with the InteractiveGraph class, nodes and edges can be positioned using the mouse, and the EditableGraph class additionally supports insertion and deletion of nodes and edges as well as their (re-)labelling through standard text-entry.
[1] https://github.com/paulbrodersen/netgraph
- Modeling project dependencies using direct graph
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Ask HN: With open source software, how do I find out where my users come from?
However, last weekend, there was a huge spike in downloads: instead of the 0-5 downloads that are typical for a normal weekend day, there were 2000 downloads, both on Saturday and Sunday [2]. I would love to know what happened here, or at least, I would like to be able to find out the next time something like this happens. Obviously, unlike a normal business, I don't control the distribution, so I can't measure the traffic with Google Analytics or similar tools.
I would love to hear how other people that have open source projects are getting their intel into their user base.
[1] https://github.com/paulbrodersen/netgraph
[2] https://pepy.tech/project/netgraph
- paulbrodersen/netgraph Python drawing utilities for publication quality plots of networks.
- Show HN: Netgraph, a Python library for visualizing networks
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?
grand - Your favorite Python graph libraries, scalable and interoperable. Graph databases in memory, and familiar graph APIs for cloud databases.
Numba - NumPy aware dynamic Python compiler using LLVM
algorithmx-python - A library for network visualization and algorithm simulation.
Dask - Parallel computing with task scheduling
chaos-theory - Playing around with chaos theory simulations. Creating equilibrium graphs and visualizing the logistic maps.
julia - The Julia Programming Language
pytransform3d - 3D transformations for Python.
RDKit - The official sources for the RDKit library
scapy - Scapy: the Python-based interactive packet manipulation program & library. Supports Python 2 & Python 3.
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
nfstream - NFStream: a Flexible Network Data Analysis Framework.
SymPy - A computer algebra system written in pure Python