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.

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.

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

The paper, "Relational inductive biases, deep learning, and graph networks," posted on the arXiv pre-print service, is authored by Peter W. Battaglia of Google's DeepMind unit, along with ...

In separate work, Williamson used machine learning to refine an old conjecture that connects graphs and polynomials. Computers have aided in mathematical research for years, as proof assistants that ...

Graph databases like TigerGraph were designed specifically to both store and analyze networks of nodes or data elements. Some standalone graph database companies like Neo4J or ArangoDB are ...

All in all, not all advanced machine learning models are black box, and for most applications, a degree of explainability is sufficient to meet legal and regulatory requirements.

Google amps up product data for shopping, while Google Cloud opens up a tool that makes easier work of artificial intelligence and machine learning.