NetworkX
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NetworkX | advent-of-code | |
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61 | 13 | |
14,178 | 13 | |
1.6% | - | |
9.6 | 0.0 | |
5 days ago | 4 months ago | |
Python | Scala | |
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.
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.
advent-of-code
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[2023 Day 5 Part 2] Haskell libraries really shine here
I didn't realize Haskell had that. I wrote a similar Scala library for 2022 Day 15 that's basically the encapsulated equivalent of a [Range a], but I really like the API of Haskell's library. Especially Haskell always handles infinite sequences well.
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-❄️- 2023 Day 5 Solutions -❄️-
[LANGUAGE: Scala] GitHub
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[2022 All Days]
Here's mine. Most of it I wrote in prior years, but refined this year. To account for problem-specific details, the functions are very generic and higher-order. It has a handful of well-known algorithms like A* and Floyd-Warshall, some handy data structures like circular buffers and intervals, and some type classes that are useful for parsing puzzle input.
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-🎄- 2022 Day 18 Solutions -🎄-
Scala 30ms + 70ms
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-🎄- 2022 Day 15 Solutions -🎄-
Scala 6.5 seconds.
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-🎄- 2022 Day 13 Solutions -🎄-
Scala
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-🎄- 2022 Day 12 Solutions -🎄-
I wrote an immutable A* in Scala a few years ago. It's not too bad if you have immutable hash maps and an immutable priority queue. Comes in handy for a lot of puzzles.
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[2022 day 4] My experience in a nutshell
Last year I made myself an input parsing library that was really nice for this problem. I just create a Pair class with 4 number members, then ask for a List[Pair] and it knows what to do. My solution.
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Best immutable priority queue for scala
I implemented my own using a pairing heap. It sped up my immutable A* considerably, but I was just using minBy on a List before that. Inserts are amortized O(1) and delete-mins are O(log n).
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What is the best way to read a text file of your input in your language of choice?
This year I'm creating a Scala library to make it easier. I specify a type like List[Int] and it summons the correct type classes to parse it into that format for me.
What are some alternatives?
Numba - NumPy aware dynamic Python compiler using LLVM
advent-of-code-data - Download Advent of Code input data with ease.
Dask - Parallel computing with task scheduling
advent-of-code-scala - Solving advent of code challenges
julia - The Julia Programming Language
aoc - 🎄 My solutions and walkthroughs for Advent of Code and more related stuff.
RDKit - The official sources for the RDKit library
advent-of-code
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
advent-of-code-2020
SymPy - A computer algebra system written in pure Python
adventofcode2022