GraphNews

#AI needs #KnowledgeGraphs to represent the physical world! See why AI needs @KnowledgeGraph for autonomous cars, for robotics, for smart homes and more...

And in the serendipitous discovery category today we have Google Cloud's "Enterprise Knowledge Graph", seemingly soft-launched a few days ago…

With the mission of building the most user-friendly graph-as-a-service that unlocks smarter insights for all, our product and engineering teams at TigerGraph have been working hard to elevate TigerGraph Cloud to the next level of ease-of-use and enterprise readiness.

The most common way to find answers on the Internet is to enter keywords in a web search engine (Google, Yandex, etc) and browse the returned results. However, in some cases the user is looking for a…

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

How to invert the data integration problem within your organisation

Success stories, shared at Ontotext’s KGF22, about building knowledge graphs in Industry, Healthcare, Life Sciences, Financials

This post by Michael Atkin is designed to demystify semantic standards and knowledge graphs for executive stakeholders

Graph neural networks (GNNs) have emerged as one of the leading solutions for ML applications. Most real-world data can be represented as graphs - see this blog for a comprehensive overview of what use cases are best solved with GNNs and their key advantages.

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

Brands invest heavily in content. Knowledge graphs make this actionable, improving resources and providing deeper insights.

The analytics workflow is broken. Every time analysts have a new question, they must submit a request to the data engineering team to add a new column to a… | 249 comments on LinkedIn

Congratulations to @TigerGraphDB on being the first to successfully pass an LDBC SNB Business Intelligence workload audit on scale factor 1000. Their setup used the @AMD EPYC 9354 (Genoa) CPUs announced today. The results are available on the LDBC website.https://t.co/X3QocaTsRR pic.twitter.com/3F3J56wa96— Linked Data Benchmark Council (@LDBCouncil) November 10, 2022

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

Learn how to boost your website's performance with semantic annotation by reading Poem Analysis case study.

This blog post was on the front page of Hacker News for a day — Link It’s been almost nine months since I left Dgraph Labs, a company that I spent

Powering biomedical search with Neo4j.

Let Andreas Blumauer, CEO of the Semantic Web Company, introduce you to a Graph-based Semantic Layer in Enterprises. Read more!

Should we all be making a Knowlege Graph part of our organisation's AI strategy? The ICLR is now recognised as one of the top conferences in deep learning… | 11 comments on LinkedIn

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

Contribute to dglai/Graph-Neural-Networks-in-Life-Sciences development by creating an account on GitHub.

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

Ontotext is a software company with offices in Europe and USA. The company was first launched in 2000 as an R&D lab within Sirma group. Today, Ontotext is predominantly focused on software solutio

Should you store business rules in your Enterprise Knowledge Graph (EKG)? This is a non-trivial question, and how you answer it might…

Excited to share our paper on graph representation learning in biomedicine and healthcare, published today in Nature Biomedical Engineering. In this…

Outlier Node Detection (OND) on graphs is widely used in financial fraudster identification, social network spammer detection, and so on. In NeurIPS 2022, we…

Identifying fraudulent behaviors is becoming increasingly more complex as technology advances and fraudsters constantly evolve new ways to exploit people, companies, and institutions. The complexity grows as companies introduce new channels, platforms, and devices for customers to engage with their brand, manage their accounts, and make transactions. Graph neural networks (GNN) are increasingly being used to identify suspicious behavior. GNNs can combine graph structures, such as email accounts, addresses, phone numbers, and purchasing behavior to find meaningful patterns and enhance fraud detection. In this video we will discuss: - Introduction to TigerGraph - Fraud Detection Challenges - Graph Model, Data Exploration, and Investigation - Visual Rules, Red Flags, and Feature Generation - TigerGraph Machine Learning Workbench: - XGBoost with Graph Features - Graph Neural Network and Explainability

GNNs derived as gradient flows minimising a learnable energy that describes attractive and repulsive forces between graph nodes.