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 ...

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

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 ...

The enterprise knowledge graph is a knowledge representation system based on graph structures. It integrates multi-source data from both internal and external sources (such as business information, ...

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 ...

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 ...

The rise of graph databases is closely related to AI's demand for data processing. AI technology requires vast amounts of structured and unstructured data, which must not only be input into ...

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.

A set of independence statements may define the independence structure of interest in a family of joint probability distributions. This structure is often captured by a graph that consists of nodes ...