GraphNews

NNeo4j, JanusGraph, and TigerGraph are the top 3 Native Property Graph Database Management Systems, according to DB-Engines ranking, but…

Thoughts on semantic layers, data transformations, data apps and the future of the data stack.

AWS developed the Deep Graph Knowledge Embedding Library (DGL-KE), a knowledge graph embedding library built on the Deep Graph Library (DGL). DGL is a scalable, high performance Python library for deep learning in graphs. This library is used by the advanced machine learning systems developed with Trumid to build a credit trading platform.

In a distributed world where data is federated in a Mesh internally and you value comes from how you collaborate externally the most…

Happy to announce that we just published our #linkeddata extension for #VSCode. Visualize, transform, validate and query your #rdf directly on your files...

Posted Grigor Aslanyan, Software Engineer, Google Patent documents typically use legal and highly technical language, with context-depend...

At IKEA we are building a knowledge graph to improve the overall experience of our customers in the physical and digital space. I like to…

You thought you knew all normal forms? Well, think again: There is also “graph normal form,” based on mathematics going back 25 years.

Co-authored with Meghana Negi, Jose Mathew, Jairaj Sathyanarayana

Ultipa has recently released v4.0 of its flagship graph database product, on top of its v3.0’s already worldleading performance. Ultipa 4.0 introduced p

Recent advancements and hot trends, July 2022 edition

Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or sequence...

Nature Biotechnology - A knowledge graph platform integrates proteomics with other omics data and biomedical databases.

Today we’ll dive into the theory and implementation of the Graph Attention Network (GAT). In a nutshell: attention rocks, graphs rock…

Feature Propagation is a simple and surprisingly efficient solution for learning on graphs with missing node features

It was a thing long before big tech firms used it to work out who knew who, explains AWS

Signal AI unveiled its new tool, a data structure that constantly tracks the major and minor events for companies that course through the news sphere each day.

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

Data Commons brings together sustainability-related data from over 100 sources — on everything from temperature to water availability.

Announcing my new (FREE) course: Basics of Graph Neural Networks (https://lnkd.in/gkP2VNYz). The focus is to give you a fast, high-level overview of common... 12 comments on LinkedIn

Cambridge Core - Communications and Signal Processing - Introduction to Graph Signal Processing

Learn how Databricks and Stardog solve the last mile challenge in democratizing data and insights, providing organizations with the enterprise-wide data fabric architecture to ask and answer complex queries across domain silos.

Scientific Data - A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks

Studying the schema of a 'legacy' data warehouse can lead to an ontology hack so easy, it almost feels like cheating.

Co-authored by Abhishek Singh, Machine Learning Engineer at Bayer Pharmaceuticals, former Microsoft, JPMorgan Chase & Co, HSBC, and by…

Emerging graph database benchmarks are already helping to overcome performance, scalability and reliability issues.

I've become increasingly convinced that it's very important to use opaque IRIs when creating ontologies and data in the semantic web space. (For those... 25 comments on LinkedIn