OSMnx is a Python package that downloads any city’s street network, buildings, bike lanes, rail, or walkable paths from OpenStreetMap and instantly turns them into clean, routable NetworkX graphs with correct topology, projected coordinates, edge lengths, bearings, and travel speeds.

Contribute to ashdehghan/NEExT development by creating an account on GitHub.

On behalf of the GraphFrames maintainers, I am happy to announce the delivery of a new release. It is a significant improvement! It improves performance and memory management: The new release provides 3-50x faster performance for all algorithms. The x5 performance improvement in Connected Components is especially important, as it allows one to perform graph-based identity resolution much faster with the new GraphFrames. All Pregel-based algorithms, such as Shortest Paths and Label Propagation, received a boost of around 3x. The new release comes with its own internal fork of Apache #Spark GraphX due to its deprecation in upstream Spark. This allows us to improve the performance of GraphX-based Label Propagation by 50x and fix memory leaks. Now, it is usable for graph processing inside Structured Streaming. New algorithms were added: New algorithms for K-core centrality, cycle detection, and maximal independent set were added. All of them are based on advanced scientific papers and operate fully in a distributed manner. New APIs: A new API for computing vertex degrees based on edge types was added. The motifs finding API now supports undirected, bidirectional, and multi-hop patterns. The #PySpark API has all the recent improvements in the Scala Core, so there is feature parity between the core and Python. Documentation improvements: The documentation has been significantly expanded, especially the sections on the arguments and parameters of the algorithms. To simplify the onboarding process for new users, the documentation website now contains an llms.txt file in the root directory. Asking an LLM chatbot or coding assistant about how to use GraphFrames is now more efficient. It is already published in Maven Central and PyPi! Blog-post: https://lnkd.in/dU4kRmSD

You can process graphs in DuckDB! In this post, we show how to use DuckDB and the DuckPGQ community extension to analyze financial data for fraudulent patterns with the SQL/PGQ graph syntax that's part of SQL:2023.

The summer has been quite busy, and we are very thrilled to announce the release of Gephi Lite v1.0! This marks for us the first version of Gephi Lite we are really proud about. You can play with i…

Construction of a large painter network with ~3000 painters using the PainterPalette dataset, connecting painters if they lived at the same place for long enough time. - me9hanics/painter-network-e...

Discover what's possible with Nodestream: a declarative framework for building, maintaining, and analyzing graph data, compatible with Neo4j & Amazon Neptune.

🚀 𝗰𝗶𝘁𝘆𝟮𝗴𝗿𝗮𝗽𝗵 𝘃𝟬.𝟭.𝟲 𝗶𝘀 𝗻𝗼𝘄 𝗹𝗶𝘃𝗲! 🚀 city2graph is a Python library that converts geospatial datasets into graphs (networks). 🔗 GitHub https://lnkd.in/gmu6bsKR What's New: 🛣️ 𝐌𝐞𝐭𝐚𝐩𝐚𝐭𝐡𝐬 𝐟𝐨𝐫 𝐇𝐞𝐭𝐞𝐫𝐨𝐠𝐞𝐧𝐞𝐨𝐮𝐬 𝐆𝐫𝐚𝐩𝐡𝐬 - Generate node connections by a variety of relations (e.g. amenity → street → street → amenity) 🗺️ 𝐂𝐨𝐧𝐭𝐢𝐠𝐮𝐢𝐭𝐲 𝐆𝐫𝐚𝐩𝐡 - Analyse spatial adjacency and neighborhood relationships with the new contiguity graph support 🔄 𝐎𝐃 𝐌𝐚𝐭𝐫𝐢𝐱 - Work seamlessly with OD matrices for migration and mobility flow analysis You can now install the latest version via pip and conda. For more examples, please see the document: https://city2graph.net/ As always, contributors are most welcome! #UrbanAnalytics #GraphAnalysis #OpenSource #DataScience #GeoSpatial #NetworkScience #UrbanPlanning #Python #SpatialAnalysis | 25 comments on LinkedIn

GraphQA is Catio’s new open-source agent for natural-language questions over architecture graphs, fusing LLMs with graph algorithms to deliver fast, structure-aware answers for dependencies, flows, and system reasoning.

Rough news on #kuzu being archived - startups are hard and Semih Salihoğlu + Prashanth Rao did so much in ways I value, and the same architectural principles we've been quietly tackling in GFQL. For those left in the lurch for an embeddable compute-tier solution to graphs, #GFQL should be pretty fascinating yet also familiar (ex: Apache Arrow-native graph queries for modern OSS ecosystems), and hopefully less stress due to a sustainable governance model. Likewise, as an oss deeptech community, we add interesting new bits like the optional record-breaking GPU mode with NVIDIA #RAPIDSAI . If you're new to it and seeing this: #GFQL, the graph dataframe-native query language, is increasingly how Graphistry, Inc. and our community work with graphs at the compute tier. Whether the data comes from a tabular ETL pipeline, a file, SQL, nosql, or a graph storage DB, GFQL makes it easy to do on-the-fly graph transforms and queries at the compute tier at sub-second speeds for graphs anywhere from 100 edges to 1,000,000,000 . Currently, we support arrow/pandas, and arrow / #nvidia #RAPIDS as the main engine modes. While we're not marketing it much yet, GFQL is already used daily by every single Graphistry user behind-the-scenes, and directly by analysts & developers at banks, startups, etc around the world. We built it because we needed an OSS compute-tier graph solution for working with modern data systems that separate storage from compute. Likewise, data is a team sport, so it is used by folks on teams who have to rapidly wrangle graphs, whether for analysis, data science, ETL, visualization, or AI. Imagine an ETL pipeline or notebook flow or web app where data comes from files, elastic search, databricks, and neo4j, and you need to do more on-the-fly graph stuff with it. We started building what became GFQL *before* Kuzu because it solves real architectural & graph productivity problems that have been challenging our team, our users, and the broader graph community for years now. Likewise, by going dataframe-native & GPU-mode from day 1, it's now a large part of how we approach GPU graph deep tech investments throughout our stack, and means it's a sustainably funded system. We are looking at bigger R&D and commercial support contracts with organizations needing to do subsecond billion+-scale with us so we can build even more, faster (hit me up if that's you!), but overall, most of our users are just like ourselves, and the day-to-day is wanting an easy OSS way to wrangle graphs in our apps & notebooks. As we continue to smooth it out (ex: we'll be adding a familiar Cypher syntax), we'll be writing about it a lot more. 4 links below: ReadTheDocs, pip install, SOTA GPU benchmarks, and original aha moment + Russell Jurney Ben Lorica 罗瑞卡 Taurean Dyer Bradley Rees

Moriondo, L., (2025). ArrowSpace: introducing Spectral Indexing for vector search. Journal of Open Source Software, 10(113), 9002, https://doi.org/10.21105/joss.09002

Introducing Brahmand: a Graph Database built on top of ClickHouse. Extending ClickHouse with native graph modeling and OpenCypher, merging OLAP speed with graph analysis. While it’s still in early development, it’s been fun writing my own Cypher parser, query planner with logical plan, analyzer, and optimizer in Rust. On the roadmap: native JSON support, bolt protocol, missing Cypher features like WITH, EXISTS, and variable-length relationship matches, along with bitmap-based optimizations and distributed cluster support. Feel free to check out the repo: https://lnkd.in/d-Bhh-qD I’d really appreciate a ⭐ if you find it useful!

Open-source tool for network discovery, visualization, and monitoring. Built with Go, FastAPI, and React, supports Docker host scanning. - karam-ajaj/atlas

Apache TinkerPop™ is a graph computing framework for graph systems and the home of the Gremlin graph traversal language. Contributorcasts allow contributors ...

pip install graphframes-py

I can't tell you how many times I've had a graph analytics idea, only to spend days trying to find decent data to test it on. 😤Sound familiar? That's why I'm excited about the talk next week by Dennis Irorere on GraphFaker - a free tool from the GraphGeeks Lab to help with the graph data problem. Good graph data is ridiculously hard to come by. It's either locked behind privacy walls, messy beyond belief, or not really relationship-centric. I've been there, we've all been there. Dennis will show us how to: - Generate realistic social networks quickly - Pull actual street network data without the headaches - Access air travel networks, Wikipedia graphs, and more 🌐 Join us on July 29 - Or register for the recording. https://lnkd.in/gBxjrWGS Whether you're in research, prototyping new features, or teaching graph algorithms, this could shorten your workflow. –And what really caught my attention is that this will allow me to focus on the fun part of testing ideas. 🤓

Unlocking graph insights with Raphtory, an advanced in-memory graph tool designed to facilitate efficient exploration of evolving networks. 🔹 Scalability & Performance: Handles large-scale graph data seamlessly, enabling fast computations. 🔹 Temporal Analysis: Investigate how networks change over time, identifying trends and key shifts. 🔹 Multi-layer Modeling: Incorporate diverse data sources into a unified, structured framework for deeper insights. 🔹 Integration: Works easily with existing pipelines via **Python APIs**, ensuring a smooth workflow for professionals. #Graphs #GraphDB #NetworkAnalysis #TemporalData https://www.raphtory.com/

Root Github Pages.

A graph database that supports more than 100+ billion data, high performance and scalability (Include OLTP Engine & REST-API & Backends) - apache/incubator-hugegraph

In this series of posts I make an appeal to organize global financial institutions and RiskTech vendors to collaborate on an open-source repository of financial risk patterns called network motifs to…

A curated list of resources for graph-related topics, including graph databases, analytics and science - graphgeeks-lab/awesome-graph-universe

Python scraper based on AI. Contribute to VinciGit00/Scrapegraph-ai development by creating an account on GitHub.

Free diagram visualization extension for JupyterLab and Jupyter Notebook.

Understanding data points through the context of their relationships

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.