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Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.
I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful.
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 ...
You can think of a graph database as a set of interconnected circles (nodes) and each node represents a person, a product, a place or ‘thing’ that we want to build into our data universe.
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 ...
A graph is a network of things, called nodes, with their relationships expressed as links, called edges. The nodes in a graph database can be tagged with properties, which are additional information ...
Graph databases are increasingly popular. In fact, according to DB-Engines graphs are the fastest growing of any database category since 2013. This growth is fueled in part because many organizations ...
Graph databases have been around in one form or another since the early oughts, but they were generally slower, more complex to work with, and more limited in terms of their applicability than ...
Imagine your database of choice blown out of the water by a startup emerging from stealth. TigerGraph may have done just that for graph databases.
Emerging graph database benchmarks are already helping to overcome performance, scalability and reliability issues.
TigerGraph Inc. is bringing its graph database to the cloud in announcement being made today at Amazon Web Services Inc.’s re:Invent conference. The company, which launched a little over a year ...
The addition of vectors provides context to the graph database for enhanced search and supports generative AI and large language models.