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Top 19 Python Graph Projects
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InfluxDB
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graph-of-thoughts
Official Implementation of "Graph of Thoughts: Solving Elaborate Problems with Large Language Models"
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burr
Build applications that make decisions (chatbots, agents, simulations, etc...). Monitor, persist, and execute on your own infrastructure.
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gnn-lspe
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
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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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gqlalchemy
GQLAlchemy is a library developed with the purpose of assisting in writing and running queries on Memgraph. GQLAlchemy supports high-level connection to Memgraph as well as modular query builder.
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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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causalgraph
A python package for modeling, persisting and visualizing causal graphs embedded in knowledge graphs.
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audio-plot-lib
This library provides graph sonification functions and has been developed for a project named "Data science and machine learning resources for screen reader users". Please refer to the project page for more details.
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pygraphv
Python library for generating dot programming language for creating graphviz graphs from python OO style code
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curves-intersection-with-gradient-descent
Plotting points that lie on the intersection of the given curves using gradient descent.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Project mention: Help with environmental parameters for a computer-controlled terrarium? | /r/SavageGarden | 2023-07-28For controlling everything, the plan is to use a Raspberry Pi running Mycodo, which will connect to the the temperature/humidity sensors, and also control the LED lights, the misting sprayers, and the heating coil.
Project mention: Q* Could Be It - Forget AlphaGO - It's Diplomacy - Peg 1 May Have Fallen - Noam Brown May Have Achieved The Improbable - Is this Q* Leak 2.0? | /r/singularity | 2023-12-08We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-ofThought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph, where units of information (“LLM thoughts”) are vertices, and edges correspond to dependencies between these vertices. This approach enables combining arbitrary LLM thoughts into synergistic outcomes, distilling the essence of whole networks of thoughts, or enhancing thoughts using feedback loops. We illustrate that GoT offers advantages over state of the art on different tasks, for example increasing the quality of sorting by 62% over ToT, while simultaneously reducing costs by >31%. We ensure that GoT is extensible with new thought transformations and thus can be used to spearhead new prompting schemes. This work brings the LLM reasoning closer to human thinking or brain mechanisms such as recurrence, both of which form complex networks. Website & code: https://github.com/spcl/graph-of-thoughts
Project mention: Building Reliable Systems Out of Unreliable Agents | news.ycombinator.com | 2024-04-10Nice, looking forward to seeing that! Someone else pointed me towards https://github.com/DAGWorks-Inc/burr/ which also seems related in case you're curious.
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: Link Prediction With node2vec in Physics Collaboration Network | dev.to | 2023-06-16As already mentioned, link prediction refers to the task of predicting missing links or links that are likely to occur in the future. In this tutorial, we will make use the of MAGE spell called node2vec. Also, we will use Memgraph to store data, and gqlalchemy to connect from a Python application. The dataset will be similar to the one used in this paper: Graph Embedding Techniques, Applications, and Performance: A Survey.
`sparklines`[1] is good for an overall low-res view. `termgraph`[2] is sometimes better for a higher-res, more capable view (but can be finicky about the data.)
[1] https://github.com/deeplook/sparklines
[2] https://github.com/mkaz/termgraph
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!
Python Graphs related posts
- Show HN: Burr: An OS Framework for Building and Debugging GenAI Apps Faster
- Graph of Thoughts: Solving Elaborate Problems with Large Language Models
- Ask HN: AI to study my DSL and then output it?
- How to Become a GQLAlchemist?
- Monitoring a Dynamic Contact Network With Online Community Detection
- NetworkX Developers, Say Farewell to the Boilerplate Code
- Understanding Data Structures & Algorithms using Real-World Libraries
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A note from our sponsor - InfluxDB
www.influxdata.com | 22 Apr 2024
Index
What are some of the best open-source Graph projects in Python? This list will help you:
Project | Stars | |
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1 | graph_nets | 5,322 |
2 | Mycodo | 2,841 |
3 | graph-of-thoughts | 1,839 |
4 | burr | 397 |
5 | pygraphblas | 338 |
6 | vizex | 225 |
7 | gnn-lspe | 214 |
8 | gqlalchemy | 205 |
9 | sparklines | 104 |
10 | grand | 75 |
11 | dodiscover | 57 |
12 | causalgraph | 40 |
13 | termcharts | 32 |
14 | audio-plot-lib | 25 |
15 | graphinate | 21 |
16 | mr-graph | 16 |
17 | pygraphv | 4 |
18 | curves-intersection-with-gradient-descent | 3 |
19 | graphviz-managed | 2 |
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