We take the opportunity to discuss the database market, graph, and beyond, with CEO and co-founder Claudius Weinberger and Head of Engineering and Machine Learning Jörg Schad. ArangoDB was ...

Amazon entered the graph database market in 2018 with Neptune ... information that modeling data as a graph can model to train Deep Learning algorithms. GNNs is considered state of the art ...

Neo4j, Neptune, and Cosmos DB are all OLTP graph databases, although Neo4j has recently added some OLAP capabilities. TigerGraph is an HTAP graph database and claims swift, deep analytics as well ...

Graph databases offer a more efficient way to model relationships and networks ... TigerGraph was designed to be able to perform deep link analytics as well as real-time online transaction ...

A research team from Kumamoto University has developed a promising deep learning model that significantly ... However, conventional Graph Neural Networks (GNNs) often struggle with accuracy ...

TigerGraph’s eBook “Native Parallel Graphs: The Next Generation of Graph Database for Real-Time Deep Link Analytics,” discusses ... IoT, AI and machine learning to make sense of ever-changing big data ...

UM researchers have developed a deep learning model to predict compound protein interactions. GraphBAN is an inductive graph-based approach. The model is all about discovering new drug candidates ...

Relational database-management systems (RDBMS) only model data ... at their center, graph databases are highly efficient when it comes to query performance, even for deep and complex queries.