GraphNews

Decentralized machine learning - where each client keeps its own data locally and uses its own computational resources to collaboratively train a model by exchanging peer-to-peer messages - is...

Azure Cosmos DB is a multi-model database service that supports various data models, including key-value, column-family, document, and…

🚀 From MSFT earnings call yesterday: Cosmos DB business grew 50% YoY in FY26 Q1! Deeply grateful for our customer's trust.❤️ Proud for Cosmos DB team to be recognized in Satya's earnings call yesterday. Also excited for our sister database SQL Hyperscale for landing nearly 75% YoY growth! Hard work leads to great results - congrats teams! Keep at it! 💪 🎉 https://lnkd.in/gGNfJtMb

Architectural Evolution from GraphX for Big Data and AI Applications

The applied ontology degree will prepare students from around the world for work in this rapidly growing interdisciplinary branch of information science.

Explore top LinkedIn content from members on a range of professional topics.

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

Knowledge Graph Anatomy 101: An Exploration

We are thrilled to be the new stewards of Dgraph at Istari Digital! looking forward to building the next data platform for digital engineering.

Learn how TypeDB combines graph semantics with strong schema and reasoning to model complex domains.

𝐑𝐞𝐮𝐬𝐢𝐧𝐠 Ontologies 𝐌𝐚𝐤𝐞𝐬 Your 𝐋𝐢𝐟𝐞 𝐄𝐚𝐬𝐢𝐞𝐫 𝐃𝐚𝐭𝐚 contains tremendous 𝐯𝐚𝐥𝐮𝐞. Unfortunately, it is often only used in a specific application, even though it would be useful in other contexts as well. However, 𝐬𝐡𝐚𝐫𝐢𝐧𝐠 data is 𝐧𝐨𝐭 a 𝐭𝐫𝐢𝐯𝐢𝐚𝐥 task. To share data effectively within an organization, we need to 𝐚𝐥𝐢𝐠𝐧 our data with a 𝐜𝐨𝐦𝐦𝐨𝐧 𝐦𝐨𝐝𝐞𝐥. The first thought that comes to mind when hearing about the concept of shared data models (also known as ontologies) is often to develop a new one from 𝐬𝐜𝐫𝐚𝐭𝐜𝐡 quickly. That allows for a fast start and often a slow, yet inevitable, 𝐜𝐡𝐚𝐨𝐬. Ontologies aim to provide a well-described and carefully disambiguated meaning. They are about finding consensus, which is a process rather than a quick win. In that regard, using standardized ontologies is tremendously helpful. (1.) Because they are the product of a collaborative process of 𝐞𝐱𝐩𝐞𝐫𝐭𝐬, many potential 𝐩𝐢𝐭𝐟𝐚𝐥𝐥𝐬 have already been considered and 𝐞𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐞𝐝. They are established and well used. (2.) They are often abstract enough to be 𝐚𝐝𝐚𝐩𝐭𝐚𝐛𝐥𝐞 to more specific domains. Reused ontologies are not a dead end. They are a 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐩𝐨𝐢𝐧𝐭 for making data your own. (3.) [𝘈𝘯𝘥 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘮𝘺 𝘧𝘢𝘷𝘰𝘳𝘪𝘵𝘦:] They are 𝐛𝐚𝐜𝐤𝐞𝐝 𝐛𝐲 one or more established 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬. Often, it is so much 𝐞𝐚𝐬𝐢𝐞𝐫 to 𝐜𝐨𝐧𝐯𝐢𝐧𝐜𝐞 people to use the standard pushed by Google or the guys who standardize the internet itself, rather than your own definitions. That does not mean that there is no need to create your own ontologies. However, your use case is likely not as unique as you think. And it might be useful to extend an existing ontology to your needs or use one as a blueprint. Want to hear more about how graphs can solve your data problems? Join our next webinar: https://lnkd.in/e6JgQzhP

Facebook, one of the world's largest social media platforms, fundamentally organizes its billions of users and their interactions as a vast social network. At the heart of this organization lies the concept of a graph—a mathematical structure consisting of nodes (or vertices) connected by edges (or

Finding the Right Level of Meaning

Knowledge Graphs (KGs) have gained attention due to their ability to represent structured and interlinked information. KGs represent knowledge in the form of relations between entities, referred to as...

The paper 'Property Graph Standards: State of the Art and Open Challenges' (VLDB 2025), Haridimos Kondylakis and his colleagues take an in-depth look at the current state of property graph standards, which form the basis of many modern graph databases. While property graphs have become a popular way to show complex, connected data (think nodes and edges with flexible key–value properties), the ecosystem is still divided. Each vendor or tool implements its own version of 'the standard', which makes interoperability, schema definition and query translation difficult. The authors review the major initiatives to standardise property graphs and demonstrate the current situation: efforts from LDBC, GQL and ISO are advancing the field, but challenges remain. The biggest gaps lie in schema constraints, data validation, and cross-system compatibility — all of which are crucial if graph systems are to become integral components of enterprise data architectures. The paper calls for a unified model in which graph structure, constraints, and semantics are shared across tools and databases. This isn't just academic. It's about ensuring that graph data can be trusted. It's also about making sure that it is portable. And that it can be used at scale. In simple terms, property graphs are maturing. The next step is not just to connect data, but to agree on how we define, validate and exchange those connections. Article: https://lnkd.in/eva_xSsT

"Semantic layer” means different things depending on who you ask. In my latest newsletter, published on Medium first this time, I look at the two definitions and how they can work together. Are you using a semantic layer? if so, which type? #SemanticLayer #DataGovernance #AnalyticsEngineering #DataandAI | 25 comments on LinkedIn

Calling all Graph Explorers! 📣 I'm excited to share that open-source Graph Explorer v2.4.0 is now released, and it includes a new SPARQL editor! Release notes: https://lnkd.in/ePhwPQ5W This means that in addition to being a powerful no-code exploration tool, you can now start your visualization and exploration by writing queries directly in SPARQL. (Gremlin & openCypher too for Property Graph workloads). This makes Graph Explorer an ideal companion for Amazon Neptune, as it supports connections via all three query languages, but you can connect to other graph databases that support these languages too. 🔹 Run it anywhere (it's open source): https://lnkd.in/ehbErxMV 🔹 Access through the AWS console in a Neptune graph notebook: https://lnkd.in/gZ7CJT8D Special thanks go to Kris McGinnes for his efforts. #AWS #AmazonNeptune #GraphExplorer #SPARQL #Gremlin #openCypher #KnowledgeGraph #OpenSource #RDF #LPG

A Practical Builders Guide

Explore the latest insights and updates from RyuGraph

AIOTI WG Standardisation Focus Group on Semantic Interoperability has prepared a report on Data to Ontology Mapping. A key challenge people face when using ontologies is […]

A first look at TypeDB 3 benchmarks, showing great performance at scale, real-world OLTP workloads, and comparisons to previous versions, and Neo4j.

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…

An open-source Text2SQL tool that transforms natural language into SQL using graph-powered schema understanding. Ask your database questions in plain English, QueryWeaver handles the weaving. - Fal...

DuckDB community extension that adds support for SQL/PGQ and graph algorithms

Explore an iterative verification and correction refinement process aimed at improving Text2Cypher performance.

Graph database implementing the RDF and SPARQL standards. Very fast and scales to hundreds of billions of triples on a single commodity machine. - ad-freiburg/qlever

When we present QLever, people often ask "how is this possible" as our speed and scale is on another dimension. We now have a page in the wiki that goes into a bit more detail on why and how this is possible. In short: • Purpose built for large scale graph data, not retrofitted • Indexing optimized for fast queries without full in-memory loading • Designed in C++ for efficiency and low overhead • Integrated full text and spatial search in the same engine • Fast interactive queries even on hundreds of billions of triples Link to the wiki page in the comments.