GraphNews

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

Knowledge graphs help understand relationships between the objects, events, situations, and concepts in your data so you can readily identify important patterns and make better decisions. This book provides tools and techniques for efficiently labeling data, modeling a knowledge graph, and using it to derive useful insights. /b In Knowledge Graphs Applied/i you will learn how to: Model knowledge graphs with an iterative top-down approach based in business needs/li Create a knowledge graph starting from ontologies, taxonomies, and structured data/li Use machine learning algorithms to hone and complete your graphs/li Build knowledge graphs from unstructured text data sources/li Reason on the knowledge graph and apply machine learning algorithms/li /ul Move beyond analyzing data and start making decisions based on useful, contextual knowledge. The cutting-edge knowledge graphs (KG) approach puts that power in your hands. In Knowledge Graphs Applied/i, you’ll discover the theory of knowledge graphs and learn how to build services that can demonstrate intelligent behavior. You’ll learn to create KGs from first principles and go hands-on to develop advisor applications for real-world domains like healthcare and finance.

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

"Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains...

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

On going beyond message-passing based graph neural networks with physics-inspired “continuous” learning models

Apollo's new platform, Supergraph, is a collection of tools that can work together to generate reports, dashboards and general answers.

Co-authors: Joshua Shinavier and Shirshanka Das

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

🔥 Attention Guided Graph Convolutional Networks for Relation Extraction 🔥 Code: https://bit.ly/3KdGOKy Graph: https://bit.ly/3FJMRmN...

Last week I had the pleasure and privilege of attending the 4th annual Knowledge Graph Conference (KGC) at Cornell Tech in NYC. In addition to contributing my own talk on “Boutique Knowledge Graphs” and participating in a panel on “Content Knowledge Graphs,” I attended two days of inspiring and insi

Logseq is pitching a privacy-focused open-source platform that helps people manage and collaborate around their knowledge bases.

ISI's Center on Knowledge Graphs research group combines artificial intelligence, the semantic web, and database integration techniques to solve complex information integration problems

"Graph Neural Networks (GNNs) are not limited to classifying nodes. One of the most popular applications is graph classification. This is a common task...

Creates the first relational knowledge graph system; adds Bob Muglia to its board

Topic Modelling: A primer and A mental model. After Philip Vollet’s post on Top2Vec, many of you pinged. So here you go. 👉 Criteria A: Discover topics... 15 comments on LinkedIn

Deep Narrative Analysis' (DNA's) long-term goals will be achieved by research and experimentation, taking small steps and refining our desig...