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

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

A research team led by Bing Qin introduces a novel method for Knowledge Graph Completion using higher-order neighbor subgraphs to address sparsity issues, demonstrating its effectiveness in a ...

If a data set has a complex underlying structure, then modeling it as a graph may reveal only a limited projection of the whole story. Emilie Purvine of the Pacific Northwest National Laboratory is ...

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.

Download KgBase's Vision Paper to learn how to build the ultimate enterprise knowledge system: a federated mesh of independently maintained no-code knowledge graphs.

Description The design, implementation, and analysis of abstract data types, data structures and their algorithms. Topics include: data and procedural abstraction, amortized data structures, trees and ...

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.

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 ...