Identity management for cybersecurity is inherently a complex graph problem due to the vast, dynamic, and interconnected nature of modern IT environments. The Fast Company Executive Board is a private ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

What Is a Graph Database? Your email has been sent Explore the concept of graph databases, their use cases, benefits, drawbacks, and popular tools. A graph database is a dynamic database management ...

Some applications are so inherently complicated that it is difficult to dig through the many layers of connected algorithms to expose the parts of the code ripe for optimization. This makes them a ...

Understanding the network organization of the brain has been a long-standing challenge for neuroscience. In the past decade, developments in graph theory have provided many new methods for ...

Gordon Scott has been an active investor and technical analyst or 20+ years. He is a Chartered Market Technician (CMT). Trading options may seem complicated, but there are tools available that can ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

Graph machine learning (or graph model), represented by graph neural networks, employs machine learning (especially deep learning) to graph data and is an important research direction in the ...

Debate and discussion around data management, analytics, BI and information governance. This is a guest blogpost by Amy Holder from Neo4j. She examines recent interest in graph databases as the basis ...