Graph algorithms and processing form the backbone of numerous applications across science and industry, ranging from social network analysis to large-scale data management.

Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...

A Facebook team has recently published a comparison of the performance of their existing Giraph-based graph processing system with the newer GraphX which is part of the popular Spark framework ...

The Graph 500 BFS benchmarks test the performance of graph analytics processing systems on synthetic Breadth-First Search (BFS) datasets, enabling comparisons of a broad range of optimized ...

Developers writing graph applications that scale into the billions of vertices and trillions and edges are forced to make trade-offs that impact the performance of the application. But thanks to new ...

Processing extremely large graphs has been and remains a challenge, but recent advances in Big Data technologies have made this task more practical. Tapad, a startup based in NYC focused on cross ...

Google designed Pregel as a graph processing engine that would be able to deliver programmability and efficiency for graph problems at scale and has been used in production at the search giant since ...

Google Cloud has updated its fully managed distributed SQL database service Spanner to add graph processing capabilities, dubbed Spanner Graph. The update is expected to help developers build ...

Integration of Python for data science, graph processing for NoSQL-like functionality, and it runs on Linux as well as Windows. At almost 30 years of age, Microsoft's flagship database has learned ...