Graphs are everywhere. From technology to finance, they often model valuable information such as people, networks, biological pathways and more. Often, scientists and technologists need to come up ...

Abstract: Graph Neural Networks (GNNs) have recently achieved remarkable success in various learning tasks involving graph-structured data. However, their application to multi-relational graph anomaly ...

Credit: Image generated by VentureBeat with FLUX-pro-1.1 Without data, enterprise AI isn't going to be successful. Getting all the data in one place and having the right type of data tools, including ...

We define a latent structure random graph as a random dot product graph (RDPG) in which the latent position distribution incorporates both probabilistic and geometric constraints, delineated by a ...

What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for ...

Neo4j Inc. today announced a new serverless offering that dramatically simplifies the deployment of its graph database offering, making it easier to use with artificial intelligence applications. Most ...

If you’re like me, you’ve heard plenty of talk about entity SEO and knowledge graphs over the past year. But when it comes to implementation, it’s not always clear which components are worth the ...

A young computer scientist and two colleagues show that searches within data structures called hash tables can be much faster than previously deemed possible. Sometime in the fall of 2021, Andrew ...

Ever since the introduction of the Google Knowledge Graph, a growing number of organizations have adopted this powerful technology to drive efficiency and effectiveness in their data management.