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 ...

A new framework integrates graph databases with real-time machine learning to enhance fraud detection and risk control in digital finance. By ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Graphs are, quite simply, a universal ...

Researchers at the University of Geneva used Graph Neural Networks to model the complex patterns of multi-drug resistant Enterobacteriaceae transmission, aiming to advance how hospitals predict and ...

Earth Scientists have used machine learning for at least three decades and the applications span is large, from remote sensing to analysis of well log data, among many others. Although machine ...

TigerGraph, provider of a leading graph analytics platform, is introducing the TigerGraph ML (Machine Learning) Workbench—a powerful toolkit that enables data scientists to significantly improve ML ...

Graph analytics platform TigerGraph has just released its new TigerGraph ML Workbench, a Jupyter-based Python development framework. TigerGraph says this machine learning toolkit “enables data ...

David Beer’s book The Tensions of Algorithmic Thinking has recently been published by Bristol University Press. In 1956, during a year-long trip to London and in his early 20s, the mathematician and ...

An MIT spinoff co-founded by robotics luminary Daniela Rus aims to build general-purpose AI systems powered by a relatively new type of AI model called a liquid neural network. The spinoff, aptly ...