Nuix Ltd (ASX: NXL) has agreed to acquire French graph-intelligence company Linkurious SAS, strengthening the company’s data analytics and visualisation...

Huge news for Cosmograph 🪐 While everyone was on Thanksgiving break, I was polishing up the next big Cosmograph update, which I'm finally ready to share! More than three years after the initial release, Cosmograph remains the only single-node web-based tool capable of visualizing graphs with 1 million points and way more than a million links due to its unique GPU Force Layout and Rendering engine cosmos.gl. However, it also had a few major weaknesses like poor memory management and limited analytical capabilities. Version 2.0 of Cosmograph solves these problems by incorporating: - DuckDB (the best in-memory analytics database); - Mosaic (the fastest cross-filtering and visual analytics framework for the web); - SQLRooms (an open-source React toolkit for human and agent collaborative analytics apps by Ilya Boyandin) as its foundation; - The latest version of cosmos.gl (our core force simulation and rendering engine that recently joined OpenJS) to give you even faster performance, more forces, and the long-awaited point-dragging functionality! What does this mean in practice? - Work with larger datasets and use SQL (thanks to WebAssembly and DuckDB); - Much better performance (filtering, timeline, changing visual properties of the graph, etc.); - Open Parquet files natively; - Save your graphs to the cloud and share them with the world easily. And if you work with ML embeddings and love Apple's Embedding Atlas (https://lnkd.in/gsWt6CNT), you'll love Cosmograph too since they have a lot in common. If all the above has excited you, go check out Cosmograph's new beautiful website, and share the news with the world 🙏 https://cosmograph.app | 41 comments on LinkedIn

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.

The world of graph technology is vast and evolving. We're here to help you make sense of the key trends and players.

Wasserstein Embedding for Graph Learning

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

Turn frontend ASTs into a Neo4j graph to see components, handlers, and events as connected behavior instead of scattered files.

LinkedIn Post | Shawn Bice A core component of our AI-first, end-to-end security platform that we announced recently is the Microsoft Sentinel graph. The term ‘graph’ is used broadly in the security industry, yet it is often misunderstood or used inaccurately.

Architectural Evolution from GraphX for Big Data and AI Applications

Free diagram visualization extension for Streamlit Apps.

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…

Text2graph is and online service for transforming free text into a knowledge graph form (nodes and relationships). The graph can be also exported using Cypher or Gremlin statements for quick import into your favourite database.

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

Ma chaine YouTube sur les graphes vient de dépasser les 3.000.000 de vues ! 🎉 🍾 Avec 73 vidéos disponibles 🖥️ , elle aide plus de 25.000 abonnés (et d'autres qui passent par hasard) à se familiariser sur un sujet qui devrait faire partie de la culture générale 📚 de tout ingénieur. Faites principalement à base d'exemples commentés, mes vidéos explorent de nombreux sujets de ce domaine à l'intersection entre les mathématiques discrètes et l'informatique. Les graphes sont "présents" partout, dans tous les systèmes composés d'éléments et de relations entre ces éléments ; ils peuvent aider à les modéliser, à mieux les maitriser et à les exploiter. Cela fait plusieurs mois maintenant que je n'ai plus rien publié sur cette chaine mais chaque jour de nouveaux venus (étudiants principalement mais pas que...) viennent découvrir ces objets simples à décrire mais si difficiles à manipuler efficacement ! Ma chaine n'est pas monétisée, je ne gagne donc pas d'argent avec. Les publicités sont ajoutées par Youtube, à leur seul profit... https://lnkd.in/exfWrPxA| 18 commentaires sur LinkedIn

Microsoft has launched a native graph data management, analytics, and visualization service. Its horizontally scalable, native graph engine empowers enterprises of all sizes with a relationship‑first way to model and explore interwoven data.

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

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The single most undervalued fact of graph theory: Every board is a graph in disguise. Here’s the 3-step mapping that turns messy “rooms” into clean, countable components. 0/ You’re given a map of walls and floor tiles. By eye, you see there are three rooms. But how do you get a computer to see them too? 1/ Start by modeling the board as a graph. Treat every floor tile as a node. Define valid moves as edges. In our case, moves are the four directions: • Up • Down • Left • Right Walls simply remove edges because you can’t step through them. 2/ Number the floor tiles arbitrarily so you can reference nodes. Now you’ve converted the board to an undirected graph. Why do this? Because two common board questions become standard graph problems. 1. “Shortest path between two tiles?” becomes “shortest path between two nodes.” 2. “How many rooms?” becomes “how many connected components?” That second one is our target. A “room” is just a maximal set of tiles reachable from each other without crossing walls. In graph terms, that’s a connected component. So the count of rooms equals the count of connected components. Here’s the practical recipe I use: • Nodes = all floor tiles. • Edges = pairs of floor tiles one step apart (U/D/L/R). • Walls = missing edges. • Rooms = connected components. • Answer = number of connected components. 3. You can run a DFS or BFS from every unvisited node and mark all reachable tiles. Each fresh start increments the room counter by one. That’s it. No heuristics, no guesswork, just graph structure doing the heavy lifting. Once you see boards as graphs, these problems stop feeling ad hoc. They become repeatable templates you can code in minutes. If this helped, repost so more people learn the “rooms = components” pattern.

Learn about how graph-wide scanning , a method that scans an entire network graph, offers a powerful solution to a major cybersecurity challenge: detecting hidden, low-signal threats like Advanced Persistent Threats (APTs) that are often missed by traditional security tools.

Think like a cyberattacker to quickly and easily identify vulnerabilities in your system with minimal query writing.