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

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 ...

The Graph, the decentralized indexing system that works much like Google for blockchains, has introduced a data standard for Web3. Called GRC-20, the standard would define how information is ...

Graphs are everywhere. From technology to finance, they often model valuable information such as people, networks, biological pathways and more. Often, scientists and technologists need to come up ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of ...

Graph theory isn’t enough. The mathematical language for talking about connections, which usually depends on networks — vertices (dots) and edges (lines connecting them) — has been an invaluable way ...