GraphNews

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.

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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

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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

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

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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.

Amazon Neptune is a fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. With Neptune, you can use open and popular graph query languages to execute powerful queries that are easy to write and perform well on connected data. You can use Neptune for […]

How graph technology can help strengthen your organization’s security program by providing the right level of context, visibility, and control over its APIs.

Structured Data chapter of the 2022 Web Almanac covering adoption and year on year change of RDFa, Opne Graph, Twitter, JSON-LD, Microdata, Facebook, Dublin Core, Microformats and microformats2 structured data.

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa...

#Google GUAC (Graph for Understanding Artifact Composition) Early stage, yet could change how the industry understands software #supplychains Free tool brings together sources of #software security metadata Collection - Ingestion - Collation - Query

Limited Preview Query Translation Tool Is Now Available for Developers to Learn and Participate in the Development Process...