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Top 17 Python graph-algorithm Projects
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graphtage
A semantic diff utility and library for tree-like files such as JSON, JSON5, XML, HTML, YAML, and CSV.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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grand
Your favorite Python graph libraries, scalable and interoperable. Graph databases in memory, and familiar graph APIs for cloud databases.
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spotify-song-recommender
A Spotify song recommendation engine built with the power of graph analytics.
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TSP_Solver
Python code for visualizations of algorithms that provide approximate solutions to TSP along with two lower bound approximations
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Built from this data... https://github.com/networkx/networkx/blob/main/examples/grap...
I'm not familiar with Pijul, and haven't finished watching this presentation, but IME the problems with modern version control tools is that they still rely on comparing lines of plain text, something we've been doing for decades. Merge conflicts are an issue because our tools are agnostic about the actual content they're tracking.
Instead, the tools should be smarter and work on the level of functions, classes, packages, sentences, paragraphs, or whatever primitive makes sense for the project and file that is being changed. In the case of code bases, they need to be aware of the language and the AST of the program. For binary files, they need to be aware of the file format and its binary structure. This would allow them to show actually meaningful diffs, and minimize the chances of conflicts, and of producing a corrupt file after an automatic merge.
There has been some research in this area, and there are a few semantic diffing tools[1,2,3], but I'm not aware of this being widely used in any VCS.
Nowadays, with all the machine learning advances, the ideal VCS should also use ML to understand the change at a deeper level, and maybe even suggest improvements. If AI can write code for me, it could surely understand what I'm trying to do, and help me so that version control is entirely hands-free, instead of having to fight with it, and be constantly aware of it, as I have to do now.
I just finished watching the presentation, and Pijul seems like an iterative improvement over Git. Nothing jumped out at me like a killer feature that would make me want to give it a try. It might be because the author focuses too much on technical details, instead of taking a step back and rethinking what a modern VCS tool should look like today.
[1]: https://semanticdiff.com/
[2]: https://github.com/trailofbits/graphtage
[3]: https://github.com/GumTreeDiff/gumtree
This is interesting to me because it's advancing the work on the notion of quantum graph problem solving.
I'm sure we've all heard how quantum computers can be used in the future to decrypt information from today. There's a lot of research out there on how QC may be able to efficiently factor large semiprimes and bust our existing cryptographic algorithms, but to me this is the more mundane side of QC.
The exciting side to me is that many graph problems, particularly whole graph problems like connectivity and shortest paths have a potential quantum advantage. This is particularly advantageous for sparse and hypersparse graphs that have billions of nodes but relatively low node degree. Language Models, chemical assay databases, proteomics, causal inference, and fraud detection are just a few problems that involve huge sparse graphs that could get a huge boost from quantum.
And to show my own bias here [1], I think the future of graph algorithms, including quantum, is expressing them in Linear Algebraic form with the GraphBLAS API. Using the GraphBLAS, you can write your algorithm in a mathematical form using the multiplication of adjacency matrices that is then synthesized to some optimal form for a given architecture.
The same code you write can then be run on a variety of backends, currently CPUs and CUDA using SuiteSparse's new JIT, but soon FPGAs and yes, quantum computers. Parallelism will become so broad and conceptually divergent that you won't even be able to conceive of an efficient hand written single function for all possible platforms.
[1] https://github.com/Graphegon/pygraphblas
Project mention: Show HN: In-memory graph "database" with NetworkX and openCypher | news.ycombinator.com | 2023-10-30Cypher is a super useful language for querying graph structures, but sometimes it's overkill to load a tiny graph into Neo4j or memgraph. We wrote this tool to act as an abstraction layer so you can query in-memory graph data -- or, using [Grand](https://github.com/aplbrain/grand), rewrite Cypher queries to run on SQLite dbs or even other graph databases that don't support Cypher out of the box. Hoping it'll be helpful to those in the network theory, graph ML, and data science communities!
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python graph-algorithms related posts
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- NetworkX – Network Analysis in Python
- NetworkX – Network Analysis in Python
- NetworkX – Network Analysis in Python
- Custom libraries and utility tools for challenges
- Vectorizing Graph Neural Networks
- Link Prediction With node2vec in Physics Collaboration Network
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Index
What are some of the best open-source graph-algorithm projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | NetworkX | 14,153 |
| 2 | graphtage | 2,317 |
| 3 | pytextrank | 2,094 |
| 4 | python-igraph | 1,232 |
| 5 | communities | 695 |
| 6 | DGFraud | 655 |
| 7 | pygraphblas | 338 |
| 8 | UGFraud | 123 |
| 9 | libmaths | 75 |
| 10 | grand | 75 |
| 11 | graphblas-algorithms | 60 |
| 12 | spotify-song-recommender | 38 |
| 13 | slack-influence-bot | 22 |
| 14 | Competitive-Python | 15 |
| 15 | BisPy | 12 |
| 16 | TSP_Solver | 7 |
| 17 | traph | 3 |
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