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 ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Our research focuses on graphs and their multiple applications: from integrating graph databases to program comprehension or from finding subgraphs efficiently to the Web of Data.

At a time when every enterprise looks to leverage generative artificial intelligence, data sites are turning their attention to graph databases and knowledge graphs. The global graph database market ...

Neo4j builds tools for creating graph databases, and today at its GraphConnect conference in New York City, it announced a new platform for developers to build graph-based applications using a common ...

In the age when data is everything to a business, managers and analysts alike are looking to emerging forms of databases to paint a clear picture of how data is delivering to their businesses. The ...

During the Build 2017 Day 2 keynote in May, Microsoft execs repeatedly referenced Microsoft Graph, the successor to Office Graph, as the key enabler of next-generation computing scenarios. Graph (the ...