According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong.

Introducing Brahmand: a Graph Database built on top of ClickHouse. Extending ClickHouse with native graph modeling and OpenCypher, merging OLAP speed with graph analysis. While it’s still in early development, it’s been fun writing my own Cypher parser, query planner with logical plan, analyzer, and optimizer in Rust. On the roadmap: native JSON support, bolt protocol, missing Cypher features like WITH, EXISTS, and variable-length relationship matches, along with bitmap-based optimizations and distributed cluster support. Feel free to check out the repo: https://lnkd.in/d-Bhh-qD I’d really appreciate a ⭐ if you find it useful!

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

Graph algorithms address a wide variety of problems by representing entities as nodes and relationships as edges. Want to find the fastest route through a city? Spot influential users on a social media platform? Or detect communities hidden in a sprawling network? Graph algorithms provide the tools

A visual introduction

You don’t need a PhD to choose the right graph representation. You just need to know what you’ll do with the graph and pick the structure that makes that fast and easy. Below is a quick, practical guide you can use today: 1. Edge List: Start here when you just need “a list of connections.” An edge list is literally a list of edges: (x, y) for directed graphs, (x, y) and (y, x) for undirected, and (x, y, w) if weighted. It shines when you process edges globally (e.g., sort by weight for Kruskal’s MST). It’s also the most compact when your graph is sparse and you don’t need constant-time lookups. When to use: • You’ll sort/filter edges (MST, connectivity batches) • You’re loading data from CSV/logs where edges arrive as rows • You want minimal structure first and you’ll convert later if needed Trade-offs: • Space: O(m) • Iterate neighbors of x: O(m) (unless you pre-index) • Check if (x, y) exists: O(m) (or O(1) with an auxiliary hash set you maintain) 2. Adjacency Matrix: Use this when instant “is there an edge?” matters. A 2D array G[x][y] stores 1 (or weight) if the edge exists, else 0 (or a sentinel like −1/∞). You get constant-time edge existence checks and very clean math/linear-algebra operations. The cost is space: O(n²) even if the graph is tiny. If memory is fine and you need O(1) membership checks, go matrix. When to use: • Dense graphs (m close to n²) • Fast membership tests dominate your workload • You’ll leverage matrix ops (spectral methods, transitive closure variations, etc.) Trade-offs: • Space: O(n²) • Check if (x, y) exists: O(1) • Iterate neighbors of x: O(n) 3. Adjacency List Choose this when you traverse from nodes to neighbors a lot. Each node stores its exact neighbors, so traversal is proportional to degree, not n. This representation is ideal for BFS/DFS, Dijkstra (with weights), and most real-world sparse graphs. Membership checks are slower than a matrix unless you keep neighbor sets. For sparse graphs and traversal-heavy algorithms, pick adjacency lists. When to use: • Graph is sparse (common in practice) • You’ll run BFS/DFS, shortest paths, topological sorts • You need O(n + m) space and fast neighbor iteration Trade-offs: • Space: O(n + m) • Iterate neighbors of x: O(deg(x)) • Check if (x, y) exists: O(deg(x)) (or O(1) if you store a set per node) ~ Pick the representation that optimizes your next operation, not some abstract ideal. Edge lists for global edge work, matrices for instant membership, lists for fast traversal. Choose deliberately, and your graph code gets both cleaner and faster. If you want a deep dive on graph representation, say “Graphs” in the comments and I’ll send it to you on DM. | 15 comments on LinkedIn

Every organization shares the same ambitions: to deliver better outcomes, increase efficiency, mitigate risks, and seize opportunities before they are lost. These ambitions underpin growth, resilience, agility, and lasting competitive advantage.  Yet most organizations struggle to harness the full value of their data to realize those ambitions. Massive volumes of granular signals flow in constantly … <p class="link-more"><a href="https://blog.fabric.microsoft.com/en-us/blog/the-foundation-for-powering-ai-driven-operations-fabric-real-time-intelligence/" class="more-link">Continue reading<span class="screen-reader-text"> “The Foundation for Powering AI-Driven Operations: Fabric Real-Time Intelligence”</span></a>

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

Calling all data scientists, developers, and managers! 📢 Looking to level up your team's knowledge of graph technology? We're excited to share the recorded 2-part training series, "Graph Tech Demystified" with the amazing Paco Nathan. This is your chance to get up to speed on graph fundamentals: In Part 1: Intro to Graph Technologies, you'll learn: - Core concepts in graph tech. - Common pitfalls and what graph technology won't solve. - Focus of graph analytics and measuring quality. 🎥 Recording https://lnkd.in/gCtCCZH5 📖 Slides https://lnkd.in/gbCnUjQN In Part 2: Advanced Topics in Graph Technologies, we explore: - Sophisticated graph patterns like motifs and probabilistic subgraphs. - Intersection of Graph Neural Networks (GNNs) and Reinforcement Learning. - Multi-agent systems and Graph RAG. 🎥 Recording https://lnkd.in/g_5B8nNC 📖 Slides https://lnkd.in/g6iMbJ_Z Insider tip: The resources alone are enough to keep you busy far longer the time it takes to watch the training!

Apache TinkerPop™ is a graph computing framework for graph systems and the home of the Gremlin graph traversal language. Contributorcasts allow contributors ...

A canonical problem in computer science is to find the shortest route to every point in a network. A new approach beats the classic algorithm taught in textbooks.

𝗛𝗼𝘁 𝘁𝗮𝗸𝗲 𝗼𝗻 𝘁𝗵𝗲 “𝗳𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗗𝗶𝗷𝗸𝘀𝘁𝗿𝗮” 𝗵𝗲𝗮𝗱𝗹𝗶𝗻𝗲𝘀: The recent result given in the paper: https://lnkd.in/dQSbqrhD is a breakthrough for theory. It beats Dijkstra’s classic worst-case bound for single-source shortest paths on directed graphs with non-negative weights. That’s big for the research community. 𝗕𝘂𝘁 𝗶𝘁 𝗱𝗼𝗲𝘀𝗻’𝘁 “𝗿𝗲𝘄𝗿𝗶𝘁𝗲” 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗿𝗼𝘂𝘁𝗶𝗻𝗴. In practice, large-scale systems (maps, logistics, ride-hailing) moved past plain Dijkstra years ago. They rely on heavy preprocessing. Contraction Hierarchies, Hub Labels and other methods are used to answer point-to-point queries in milliseconds, even on large, continental networks. 𝗪𝗵𝘆 𝘁𝗵𝗲 𝗱𝗶𝘀𝗰𝗼𝗻𝗻𝗲𝗰𝘁?  • Different goals: The paper targets single-source shortest paths; production prioritizes point-to-point queries at interactive latencies.  • Asymptotics vs. constants: Beating O(m + n log n) matters in principle, but real systems live and die by constants, cache behavior, and integration with traffic/turn costs.  • Preprocessing wins: Once you allow preprocessing, the speedups from hierarchical/labeling methods dwarf Dijkstra and likely any drop-in replacement without preprocessing. We should celebrate the theoretical advance and keep an eye on practical implementations. Just don’t confuse a sorting-barrier result with an immediate upgrade for Google Maps. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: Great theory milestone. Production routing already “changed the rules” years ago with preprocessing and smart graph engineering.

Guest Author: André Vermeij, Founder of Kenedict Innovation Analytics & Developer of...

🚀 Faster than Dijkstra? Tsinghua University’s new shortest path algorithm just rewrite the rules of graph traversal. For 65+ years, Dijkstra’s algorithm was the gold standard for finding shortest paths in weighted graphs. But now, a team from Tsinghua University has introduced a recursive partial ordering method that outperforms Dijkstra—especially on directed graphs. 🔍 What’s different?  Instead of sorting all vertices by distance (which adds log-time overhead), this new approach uses a clever recursive structure that breaks the O(m + n log n) barrier ✨.  It’s faster, leaner, and already winning awards at STOC 2025 🏆. 📍 Why it matters:  Think Google Maps, Uber routing, disaster evacuation planning, circuit design—any system that relies on real-time pathfinding across massive graphs. Paper ➡ https://lnkd.in/dGTdRj2X #Algorithms #ComputerScience #Engineering #Dijkstra #routing #planning #logistic | 34 comments on LinkedIn

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

Reforms on the "Shortest Path" Algorithm, Parallels with Modular Data Architectures, and Diving Into Key Components: Product Buckets, Semantic Spine, & Insight Routers

Sliding puzzles, from the classic 8-puzzle to larger grids, are played by moving tiles one step at a time until the numbers fall into…

🔗 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"