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.

Its dynamic computation graph helps developers build and modify models on the fly, making it a preferred choice for AI researchers, data scientists, and engineers working in neural networks. You ...

The 10 hottest data science and machine learning tools include MLflow 3.0, PyTorch, Snowflake Data Science Agent and ...

Deep learning models are represented in PyTorch as Dynamic Computation Graphs (DCGs). Unlike with pre-constructed static ...

More information: Xiaorui Su et al, Interpretable identification of cancer genes across biological networks via ...

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 ...

The cloud’s place in the data environment is growing, and TigerGraph wants to bolster its role. Today, the company rolled out several new features so cloud users can deliver more analytics and ...

In this collection we highlight a selection of recent computational studies published in Nature Communications, featuring advances in computational chemistry methods and progress towards machine ...

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.