Onager: A DuckDB extension for graph data analytics

Here is a really nice NotebookLM deck that explains how my Open Source HTML Graph service works

Descriptive set theorists study the niche mathematics of infinity. Now, they’ve shown that their problems can be rewritten in the concrete language of algorithms.

Discover what’s new this week in graph technology, including a big acquisition, a pre-seed investment, a 360-degree take on Connected Data London, and more.

A Gremlin-based implementation of the LDBC Social Network Benchmark (SNB) Interactive v1 workload for TinkerPop-compatible graph databases.

TinkerBench is a benchmarking tool designed for graph databases based on Apache TinkerPop. It provides an efficient way to measure Gremlin query language performance in an easy and flexible manner. TinkerBench is created and maintained by Aerospike

proposed model GRAPHTRIALS identifies key processes for visually proving an assertion about a given graph in an adversarial setting. The prosecution lawyer, a software or a human (assisted by software), intends to highlight evidence for a graph being accused of satisfying an assertion by usage of a visual certificate drawing. To convince the judge, the human audience of the drawing, the visual certificate must guide the judge’s perception to form a mental model which makes the assertion easy to validate. Further, the visual certificate must be unimpeachable as a defense lawyer, yet another piece of software or a human adversary, and checks for reasons to doubt the validity of the certificate which may influence the judge’s verdict

But what if a broken link could become a moment of discovery?.

Analyzing graph data is becoming increasingly important in the modern era, where relationships between people, systems, and transactions matter more than ever. To uncover these hidden connections, we rely on powerful graph analysis tools—and two standout technologies are NetworkX and GraphFrames. These tools help transform complex relationships into meaningful insights, especially in fields like fraud detection, cybersecurity, recommendation systems, and social network analysis. NetworkX vs. GraphFrames — Two Powerful Tools for Different Needs. NetworkX: -A flexible, easy-to-use Python library. -Ideal for small to medium graphs and quick experiments. -Rich algorithms for centrality, clustering, and pathfinding. -Great for research, education, and local analytics. -But not designed for massive or distributed datasets. GraphFrames: -Built on Apache Spark using DataFrames. -Designed for big data and distributed computing. -Can analyze graphs with millions or billions of edges. -Integrates with the full Spark ecosystem (SQL, MLlib, streaming). -Perfect for enterprise-scale, real-time analytics In the financial industry, fraudulent transactions often hide within complex webs of relationships. A financial institution modeled account holders and transactions using GraphFrames—treating people as vertices and transactions as edges. This graph-based model revealed unusual patterns such as clusters of accounts frequently connected through suspicious transfers. Graph analytics provides a new way to understand data—not just as rows and columns, but as a network of connected insights. As data relationships continue to grow in complexity, tools like NetworkX and GraphFrames become essential for data engineers, analysts, and AI specialists. #GraphFrames #NetworkX #GraphAnalytics #ApacheSpark #BigData #DataEngineering #DataScience #MachineLearning #FraudDetection #AI #FinTech #Python #Analytics #BusinessIntelligence #Spark

An Entity Resolved Semiconductor Business Graph

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.