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 ...

Depending on the underlying graph, you also need to handle cycles intelligently. In social networks, mutual relationships are ...

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 ...

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 ...

Enables Real-Time, Zero-ETL Graph Queries on the Databricks Data Intelligence Platform Databricks Managed Iceberg Tables, launching in Public Preview at this year’s Data + AI Summit, offers full ...

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 ...

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 ...

One key platform feature of Office 365 that is not well understood is Microsoft Graph. Building on the information stored in Office 365 and in Microsoft’s systems management and identity tools, ...