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

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 ...

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 ...

Graph processing is hot right now in anomaly and fraud detection, recommenders, social network analysis, graph search, and various forms of access control. Why those specific applications?

Graph processing at hyperscale has historically been a challenge because of the sheer complexity of algorithms and graph workflows. Alibaba has been tackling this issue via a project called GraphScope ...

TigerGraph boasts a new parallel architecture for native graph storage and processing that puts it ahead of the competition, and has the benchmarks and the use cases to back this up.

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 ...