Graph technology is well on its way from a fringe domain to going mainstream. We take a look at the state of the union in graph, featuring Neo4j's latest release and insights as well as data and ...

The row and column structure of a relational database, with its "join" operations and the like, didn't cut it. Also: Big data in action: Using graph databases to drive new customer insights ...

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 ...

The appetite for connected data is fueling a shift from traditional relational databases to interconnected graph-based models. This evolution promises deeper insights and can facilitate a more ...

Key-value, document-oriented, column family, graph, relational… Today we seem to have as many kinds of databases as there are kinds of data. While this may make choosing a database harder, it ...

In the world of relational databases, SQL (structured query language) has been the dominant standard for years. It defines a way to search for the rows in a table that match specific criteria. If ...

A research team led by Zhengtao Yu introduces the Element Relational Graph-Augmented Multi-Granularity Contextualized Encoder (ERGM) for document-level event role filler extraction, outperforming ...

First is Node2Vec, a popular graph embedding algorithm that uses neural networks to learn continuous feature representations for nodes, which can then be used for downstream machine learning tasks.