Dynamic graph algorithms and data structures represent a vital research frontier in computer science, underpinning applications from network analysis to real-time system monitoring.

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks.

Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

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.

Graphs naturally generalize unstructured vectorial data and structured data such as time series, images or bags of entities. The goal of this course is to provide an overview of the fundamental ...

Informatica's Gaurav Pathak and Fivetran's George Fraser discussed the evolving data stack and the role of metadata knowledge graphs during Supercloud 7.

A graph database is a dynamic database management system uniquely structured to manage complex and interconnected data. Unlike traditional databases organized in rows and columns, graph databases ...

József Balogh, Robert Morris, Wojciech Samotij, Lutz Warnke, The typical structure of sparse Kr+1-free graphs, Transactions of the American Mathematical Society, Vol. 368, No. 9 (September 2016), pp.