Graph analytics is a hot topic, but what does it mean? At the DC GraphTour, I learned the difference between graph queries, graph algorithms, and graph analytics. Next up: San Francisco GraphTour.

Dr. Alin Deutsch of UC San Diego explains in a Q&A why graph database algorithms will become the driving force behind the next generation of AI and machine learning apps.

Two computer scientists found — in the unlikeliest of places — just the idea they needed to make a big leap in graph theory.

The four pillars of graph adoption This confluence of graph analytics, graph databases, graph data science, machine learning, and knowledge graphs is what makes graph a foundational technology.

Nvidia has expanded its support of NetworkX graph analytic algorithms in RAPIDS, its open source library for accelerated computing. The expansion means data scientists can run 40-plus NetworkX ...

Graph algorithms and sparsification techniques have emerged as pivotal tools in the analysis and optimisation of complex networked systems.

It also compresses data up to 10 times. TigerGraph also supports different graph partitioning algorithms enabling it to split very large graphs over a distributed architecture.

Graph Algorithms: Computational procedures designed to solve problems related to graph structures, encompassing processes such as traversal, shortest path determination, and network flow analysis.

The graph database is popular with social networks, but there's no reason to limit it to tracking people and their friendships.

Published research demonstrates that reduced link graphs are an effective way to rank links. This article discusses research and patents that explain how reduced link graph algorithms work.