I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful.

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.

After relational, key-value, document, columnar, and time-series databases, the latest link in this evolutionary proliferation of data structures is graph.

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.

Direct Acyclic Graph or DAG may be it. What is DAG? DAG is a directed graph data structure that uses a topological ordering. The sequence can only go from earlier to later.

Knowledge graphs are a valuable tool that organizations can use to manage the vast amounts of data they collect, store, and analyze. At Data Summit 2023, Joseph Hilger, COO, Enterprise Knowledge LLC ...

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

Graphs -- data structures that show the relationship among objects -- are highly versatile. It's easy to imagine a graph depicting a social media network's web of connections. But graphs are also ...