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 ...

Understanding the relationships in graph database theory allows us to work with the new 'shape' of data itself. Businesspeople like graphs. C-suite executives are fond of pie charts, Venn diagrams, ...

Polyglot persistence is becoming the norm in big data. Gone are the days when relational databases were the one store to rule them all; now the notion of using stores with data models that best align ...

Most enterprise software has a contingent of zealots, people so steeped in the technology that they are convinced it is the be-all and end-all, or those who have taken so many certification exams that ...

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 ...

Graph databases represent one of the fastest-growing areas in the database market. MarketsandMarkets’ report on graph databases predicts that graph databases will grow from $1.9 billion in 2021 to ...

Graph databases are an 18th century concept with a host of modern applications. Used for tasks as diverse as dating sites and fraud detection, graph technology works by looking at relationships, not ...