Graph technology has become a requirement for the modern enterprise. Companies in virtually every industry, from healthcare to energy to financial services, are applying the power of graph analytics ...

How would you feel if you saw demand for your favorite topic — which also happens to be your line of business — grow 1,000% in just two years’ time? Vindicated, overjoyed, and a bit overstretched in ...

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 ...

REDWOOD CITY, Calif., March 16, 2020 – TigerGraph, a scalable graph database for the enterprise, unveiled TigerGraph 3.0, which delivers the power of scalable graph database and analytics to everyone ...

Graph databases such as Neo4j, TigerGraph, Amazon Neptune, the graph portion of Azure Cosmos DB, and AnzoGraph, the subject of this review, offer a natural representation of data that is primarily ...

In the modern world of data-driven applications we are at a fascinating point at which both fully formed products and powerful components are being offered to us at a breathtaking pace. The question ...

As data ecosystems become more complex, organizations are looking for advanced tools and technologies to manage and derive value from diverse and interconnected data sources. Knowledge graphs provide ...

Probabilistic graphs and uncertain data analysis represent a rapidly evolving research domain that seeks to reconcile the inherent imprecision of real-world data with robust computational models. By ...

Graph databases are the fastest growing category in all of data management, according to DB-Engines.com, a database consultancy. Since seeing early adoption by companies including Twitter, Facebook ...