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!

Every year, businesses and consumers lose billions of dollars to fraud, with consumers reporting $12.5 billion lost to fraud in 2024, a 25% increase year over year. People who commit fraud often work together in organized fraud networks, running many different schemes that companies struggle to detect and stop. In this post, we discuss how to use Amazon Neptune Analytics, a memory-optimized graph database engine for analytics, and GraphStorm, a scalable open source graph machine learning (ML) library, to build a fraud analysis pipeline with AWS services.

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

A curated list of resources for graph-related topics, including graph databases, analytics and science - graphgeeks-lab/awesome-graph-universe

The Rise of Graph Jobs, The Disappearance of Graph Technology? Join me for this fun presentation at KGC on May 8, 2024, in NYC, where I delve into a "State of…

Building enterprise knowledge graphs as property graphs of billions of nodes / edges holding organizational knowledge to power your…

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.

Here's why and how a startup founded by a duo of researchers some months back is attracting big enterprise clients and serious funding

GDS 2.0 and AuraDS from Neo4j bring graph data science one step closer to mainstream adoption.

In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies and processes that can support this transition, and what they are about.

Graphs are everywhere. That has been the motto of graph aficionados for years, and now the world seems to be waking up to this.

Amazon Neptune just added another query language, openCypher, to its arsenal. That may not sound like a big deal in and of itself, but coupled with updates in machine learning and data science features, it points towards the future of graph databases.

vendors company,url,SPARQL,RDF-Star,Gremlin,Cypher,misc query,distrib,open source,parent,speaker,notes AgensGraph,a href="https://bitnine.net/agensgraph/"https://bitnine.net/agensgraph//a,Y,SQL,Bitnine AllegroGraph,a href="https://allegrograph.com/products/allegrograph/"https://allegrograp...

Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics. — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national

TigerGraph unveiled a new tool that provides users with a dedicated, open source environment for building machine learning models with graph databases.

TigerGraph's ML Workbench should enable data scientists to build deep-learning AI models using connected data directly from the business.

Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologie...

With a $250,000 first prize, entrepreneurs, academics, engineers, scientists can create their own problem statement focused on a topic of their choosing.

Can Machine Learning Do Symbolic Manipulation?  I spent some time over the holidays engaged in a fascinating online conversation. The gist of it was a variation of an argument that has been going on in the realm of artificial intelligence from the time of Minsky and Seymour Papert: Whether it is possible for neural networks to… Read More »DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation?

How and why the best companies are adopting Graph Visual Analytics, Graph AI, and Graph Neural Networks. By Leo Meyerovich and Ben Lorica. [A version of this post originally appeared on the Graphistry blog.] In this post, we highlight the current state of Graph Intelligence, a new technology category around new tools and techniques forContinue reading "What is Graph Intelligence?"

Papers With Code highlights trending Machine Learning research and the code to implement it.

Open Source Tool - Cybersecurity Graph Database in Neo4j - GitHub - amberzovitis/GraphKer: Open Source Tool - Cybersecurity Graph Database in Neo4j

Dr. Alin Deutsch of UC San Diego explains in a Q&A why graph database algorithms will become the driving force behind the next generation of AI and machine learning apps.

Memgraph, the streaming graph application platform, today announced Memgraph 2.0, the public launch of its source-available platform, which makes it easy

🤖 A Python library for learning and evaluating knowledge graph embeddings - GitHub - pykeen/pykeen: 🤖 A Python library for learning and evaluating knowledge graph embeddings

Graph4nlp is the library for the easy use of Graph Neural Networks for NLP. It provides both full implementations of state-of-the-art models for data scientists... 26 comments on LinkedIn

How simple structured data trumps clever machine learning