Use graph databases in ML, GenAI, and NLP workflows from feature engineering and embeddings to neural network modeling.
There are a lot of opportunities of using graph databases with ML workflows. From providing additional features to using graph algorithms for unsupervised learning. Graphs can also represent neural networks, and approaches like graph2vec and deepgl offer interesting insights via embedding extraction and clustering.
This is an area with active development, so feel free to bring your questions and ideas.
For specific questions on graph algorithms ask in Neo4j Graph Platform > Graph Algorithms/Graph Data Science