🔗 How's your Link Prediction going? Did you know that the best algorithm for link prediction can vary by network? Slight differences in your graph data, and you may be better off with a new approach. Join us for an exclusive talk on August 28th to learn how to find the right link prediction model and, ultimately, get to more complete graph data. Researchers Bisman Singh and Aaron Clauset will share a new (just published!) meta-learning approach that uses a network's own structural features to automatically select the optimal link prediction algorithm! This is a must-attend event for any data scientist or researcher who wants to eliminate exhaustive benchmarking while getting more accurate predictions. The code will be made public, so you can put these insights into practice immediately. 🤓 Ready to really geek out? Register now: https://lnkd.in/g38EfQ2s

pip install graphframes-py

Temporal graph modeling, enhanced by our time-forest approach, tackles the dynamic nature of credit fraud.

The future of trustworthy AI. Powered by graphs. data² has secured a groundbreaking patent for explainable AI powered by graphs. 🚨 AI hallucinations destroy trust. That's not acceptable when lives and missions are at stake. While others rush to patch traditional RAG systems, we've engineered a fundamentally different approach. Our patented innovation delivers what leaders demand: 🔍 **Complete Transparency** - Watch AI traverse relationship paths in real-time - No more black box decisions 📊 **Evidence You Can Trust** - Every conclusion links to source data - Full citation trails for audit readiness How did we build it? 🔗 **Graph-Based Architecture** - Knowledge graphs capture critical relationships traditional RAG misses - Every connection adds context and validates accuracy This isn't just innovation for innovation's sake. At data² we are solving critical challenges across: ↳ Intelligence operations requiring all-source validation ↳ Cyber threat analysis demanding instant verification ↳ Energy infrastructure decisions where safety is paramount ↳ Financial investigations tracking complex money flows ↳ Supply chain operations in contested environments While others promise AI accuracy, we've patented how to prove it. 💬 Interested in learning more? Reach out directly. 🔔 Follow me Daniel Bukowski for daily insights about delivering transparent AI with graph technology. | 90 comments on LinkedIn

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. 🤓

Maria Chudnovsky reflects on her journey in graph theory, her groundbreaking solution to the long-standing perfect graph problem, and the unexpected ways this abstract field intersects with everyday life.

This blog post provides a hands-on guide for AI engineers and developers on how to build an initial KYC agent prototype with the OpenAI Agents SDK. We'll explore how to equip our agent with a suite of tools (including MCP Server tools) to uncover and investigate potential fraud patterns.

Chasing Online Fraud — A Constantly Changing Problem

How Legal Documents and AI Reveal the Hidden Networks Behind Crime

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

In large Knowledge Graphs (KGs), measuring the importance of each entity is essential for improving search, navigation, and reasoning…

Uniquely Pioneering Graph Analytics Combined With Deep Search Galway, Ireland – 24th June, 2025 — Siren, the all-in-one investigation company, today announced its adoption of Graph Query Language (GQL), the world’s first ISO-standard query language for graphs, made public in 2024. With this move, Siren becomes the first investigative platform to offer seamless, standards-based graph … Continue reading "Siren Adopts ISO-Standard GQL to Power the Next Generation of Graph Intelligence"

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/

Entity resolution systems face challenges with dense, interconnected graphs, and clique-based graph compression offers an efficient solution by reducing storage overhead and improving system performance during data deletion and reprocessing.

6 years after, where are we? Interested by your feedback concerning the evolution of the graph technologies landscape and about what the current landscape is. https://lnkd.in/eEPkExH | 25 comments on LinkedIn

One of the few things I love as much as Knowledge Graphs (KGs) is basketball (and LEGO bricks, but that’s another story)! So it made…

The best talk during RSA Conference in my mind is: Graphs and Algebras of Defense John Lambert Corporate Vice President, CISO Microsoft What differentiates industry visionary, and average people, is the capability to abstract the theory from practice. John came up with an elegant abstraction of graph “algebra” for cybersecurity defense, that resonates well with my PhD thesis on manifold leanring and graph embedding. The way the algebra operator on cybersecurity graphs is inspiring. I hope more innovations can be sparked by such elegant framework. Leo Meyerovich Alexander Morisse, PhD #GraphThePlanet | 13 comments on LinkedIn

📣 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

To all the knowledge graph enthusiasts who've felt for a while that "graphs are the way to go" when it comes to enabling "intelligence," it was interesting to read Anthropic's "Tracing the thoughts of a large language model" - if you believe that LLMs need graphs to reason, you are right and now you have evidence: Claude answers questions by building and traversing a graph (in latent space) before it translates it back to language: https://lnkd.in/eWFWwfN4 | 20 comments on LinkedIn

Root Github Pages.

Uncover data's hidden connections using graph analytics in BigQuery. This session shows how to use BigQuery's scalable infrastructure for graph analysis directly in your data warehouse. Identify patterns, connections, and influences for fraud detection, drug discovery, social network analysis, and recommendation engines. Join us to explore the latest innovations in graphs and see real-world examples. Transform your data into actionable insights with BigQuery's powerful graph capabilities.

Learn about different types of graphs and their applications in data management and AI, as well as common misconceptions, in this article by Lulit Tesfaye.

Deep stuff! We uncovered a startling link between #entropy, a bedrock concept in #physics, and how #AI can discover new ideas without stagnating. In an era… | 41 comments on LinkedIn

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

Ashley Faith hosted Amy Hodler and me on a recent episode about “2025 Predictions” — here are notes I prepared, to review in 2026…

The Evolution of Intelligent Recommendations with Agentic Graph Systems ➿ Agentic graph systems for recommendation represent a sophisticated fusion of…

Graph is increasingly driving the Agentic space, which I see as being a very good sign. Recently, a programmer asked why graphs would be superior to using…

At Microsoft, we strive to constantly learn and share our insights across innovation, culture and governance. Today, as we reflect on our progress since launching the Secure Future Initiative one year ago, I want to share a little more about our vision for a graph-powered platform future.