Probabilistic graphs and uncertain data analysis represent a rapidly evolving research domain that seeks to reconcile the inherent imprecision of real-world data with robust computational models. By ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Rajiv Shesh is the Chief Revenue Officer at HCLSoftware where he leads revenue growth & customer advocacy for Products & Platforms division. What’s really powering AI? High-quality data—foundational ...

Ever since the introduction of the Google Knowledge Graph, a growing number of organizations have adopted this powerful technology to drive efficiency and effectiveness in their data management.

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Bar graphs seem like one of the simplest ways to represent data. Many people assume that the longer the bar, the bigger the number it represents. Sometimes bar graphs represent an average not a total ...

Quantum computers promise to speed calculations dramatically in some key areas such as computational chemistry and high-speed networking. But they're so different from today's computers that ...