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.

Free diagram visualization extension for Streamlit Apps.

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...

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

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

I’m proud to share an exciting piece of work by my PhD student, Simon van Wageningen, whom I have the pleasure of supervising. Simon asked a bold question that challenges the state of the art in our field! A bit of background first: together with Simon, we study network visualizations — those diagrams made of dots and lines. They’re more than just pretty pictures: they help us gain intuition about the structure of networks around us, such as social networks, protein networks, or even money-laundering networks 😉. But how do we know if a visualization really shows the structure well? That’s where quality metrics come in — mathematical functions designed to measure the “goodness” of a network visualization. Many of these metrics correlate nicely with human intuition. Yet, in our community, there has long been a belief — more of a tacit knowledge — that these metrics fail in certain cases. This is exactly where Simon’s work comes in: he set out to make this tacit knowledge explicit. Take a look at the dancing man and the network in the slider — they represent the same network with very similar quality metric values. And yet, the dancing man clearly does not don’t show the network's structure. This tells us something important: we can’t blindly rely on quality metrics. Simon’s work will be presented at the International Symposium on Graph Drawing and Network Visualization in Norrköping, Sweden this year. 🎉 If you’d like to dive deeper, here’s the link to the GitHub repository https://lnkd.in/eqw3nYmZ #graphdrawing #networkvisualization #qualitymetrics #research with Simon van Wageningen and Alex Telea | 13 comments on LinkedIn

Chasing Online Fraud — A Constantly Changing Problem

Discover how Linkurious and Spanner Graph empower Google Cloud users with scalable, user-friendly graph analytics and visualization.

📣 Byte #21: For those of you who want to visualize their graphs inside Jupyter notebooks - we have an exciting development! We recently released an integration with yWorks, who extended their yFiles Jupyter Graphs widget to support Kuzu databases! ✅ Once a Kuzu graph is created, we can instantiate the yFiles Jupyter KuzuGraphWidget, and use the `show_cypher` method to display a subgraph using regular Cypher queries. ✅ There are numerous custom layouts in the yFiles widget (tree, hierarchical, orthogonal, etc.). Give them a try! Here's an example of the tree layout, which is great for visualizing data like this that has rich tree structures. We can see the two-degree mentors of Christian Christiansen, a Nobel prize-winning laureate, in this example. ✅ You can customize the appearance of the nodes in the widget through `add_node_configuration` method. This way, you can display what you're looking for as you iterate through your graph building process. ✅ The Kuzu-yFiles integration is open source and you can begin using it right away for your own interactive visualizations. Give it a try and share around with fellow graph enthusiasts! pip install yfiles-jupyter-graphs-for-kuzu Docs page: https://lnkd.in/g97uSKRe GitHub repo: https://lnkd.in/gjA6ZjiF

As I did my PhD in network and data science, graphs are really close to me. Now, if you would like to get a tast of why these fields are so exciting and…

Simplifying Complex Graphs with Nested Nodes and Edges: A Challenge Navigating massive graphs with nested nodes, including groups within groups, can feel like… | 34 comments on LinkedIn

Gemini 1.5 is so powerful that it can read all the Harry Potter books at once and generate a graph of the characters with perfect accuracy. All the books have… | 146 comments on LinkedIn

🧠 Visualizing Automatically Generated AI Semantic Networks🧠 Yesterday I shared an #automation that leverages #Claude3 in an iterative process to create… | 17 comments on LinkedIn

Code for all my videos: https://github.com/sponsors/adumb-codes/Twitter: https://twitter.com/adumb_codesA deep dive into the network of Wikipedia and some of...

By Dr. Verónica Espinoza. 2023

Yesterday, I re-shared a huge list of Python visualisation tools - and now, here comes a list of network visualisation tools (these two lists certainly… | 74 comments on LinkedIn