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.

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.

Interesting, and something I've been suspecting for a while. In semantics, one of the things that eventually all ontologists encounter is the fact that a human being cannot readily visualize large graphs, or even moderate-sized ones. Graphs are networks, and most networks can at best only be navigated from within. All graphs are collections of interconnected links between things; semantically we simplify those links, abstract them to a certain degree, but links in turn are also themselves describable as graphs, and when describing a system, those links in turn are dynamic and changing. One thing that marks the boundary between algorithms and intentional systems is complexity. Intentional systems, such as large language models, are more complex than the human brain can understand. We have to use computers to handle this complexity, because it is outside of the scope of our human wetware. This is a humbling realisation, and is the real power of AI; the point where we reached a level of complexity that is no longer completely comprehensible to mere mortals. This isn't going to change, regardless of what model we use for that AI. | 15 comments on LinkedIn

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.

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

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

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

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…

Latest findings in pre-training graphs and using them for link recommendation

Python scraper based on AI. Contribute to VinciGit00/Scrapegraph-ai development by creating an account on GitHub.

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

How to create synthetic datasets to improve graph-based anomaly detection models? Amazon researchers developed a new method for synthesizing training data for…

Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex network science has significantly enhanced our understanding of the statistical mechanics, structures, and dynamics underlying real-world networks. Despite these advancements, there remain considerable challenges in exploring more realistic systems and enhancing practical applications. The emergence of artificial intelligence (AI) technologies, coupled with the abundance of diverse real-world network data, has heralded a new era in complex network science research. This survey aims to systematically address the potential advantages of AI in overcoming the lingering challenges of complex network research. It endeavors to summarize the pivotal research problems and provide an exhaustive review of the corresponding methodologies and applications. Through this comprehensive survey-the first of its kind on AI for complex networks-we expect to provide valuable insights that will drive further research and advancement in this interdisciplinary field.

Translating visual networks into graphs using AI is a game changer for accelerating analytics on information that is historically difficult to access.

Links * Python Examples * JS Examples * YouTube Last week we highlighted LangGraph - a new package (available in both Python and JS) to better enable creation of LLM workflows containing cycles, which are a critical component of most agent runtimes. As a part of the launch, we highlighted two simple runtimes:

⚡ Building language agents as graphs ⚡

Leveraging Graph Algorithms to Enable Responsible AI Reasoning 💡 Large language models have shown immense promise in their ability to generate remarkably…

Graph-based data is ubiquitous in enterprise, across the industry verticals, and increasingly needed for machine learning use cases…

Understanding data points through the context of their relationships