First is Node2Vec, a popular graph embedding algorithm that uses neural networks to learn continuous feature representations for nodes, which can then be used for downstream machine learning tasks.

This article explores what knowledge graphs are, why they are becoming a favourable data storage format, and discusses their potential to improve artificial intelligence and machine learning ...

A s 2022 dawns, knowledge graphs bear the dubious distinction of being at the epicenter of AI and machine learning for two reasons. One is that, unassisted, they are one of the myriad manifestations ...

Deep learning models are represented in PyTorch as Dynamic Computation Graphs (DCGs). Unlike with pre-constructed static graphs, the structure of the neural network is built and modified on the ...

Combining graphs and machine learning has been getting a lot of attention lately, especially since the work published by researchers from DeepMind, Google Brain, MIT, and the University of Edinburgh.

Understanding, Knowing, and Connecting via Knowledge GraphsKnowledge graphs grant us new and different ways of visualizing our data. The technology connects disparate entities and surfaces the ...

Machine learning-based neural network potentials often cannot describe long-range interactions. Here the authors present an approach for building neural network potentials that can describe the ...

Amazon Neptune just added another query language, openCypher, to its arsenal. That may not sound like a big deal in and of itself, but coupled with updates in machine learning and data science ...