The study of SimRank and other similarity measures in large graphs is central to understanding how structural patterns can reveal latent relationships among data entities. SimRank posits that two ...

In recent years, knowledge graphs have become an important tool for organizing and accessing large volumes of enterprise data in diverse industries — from healthcare to industrial, to banking and ...

Graph Neural Networks (GNNs) have gained widespread adoption in recommendation systems. When it comes to processing large graphs, GNNs may encounter the scalability issue stemming from their ...

If you are interested in learning how to build knowledge graphs using artificial intelligence and specifically large language models (LLM). Johannes Jolkkonen has created a fantastic tutorial that ...

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

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 ...

It's been quite a while since there's been word regarding Microsoft Research's "Project Trinity," its graph database and computing platform. But this week, Trinity -- now known as Graph Engine -- was ...

Victor Lee is director of product management at TigerGraph. Graph databases excel at answering complex questions about relationships in large data sets. But they hit a wall—in terms of both ...