We've released new features in Neo4j, including the ability to train and store up to 3 ML models in the community edition, making it more useful for users just getting started. This release also introduced a ScaleProperties procedure to transform and scale node properties, Graph Filtering/Subgraph Projections to create subgraphs from an in-memory graph by filtering on node or relationship properties, improved Graph Embeddings with Node2Vec and graphSAGE, better Supervised ML Pipelines with support for saving, publishing, and restoring trained models, and Administrative Capabilities for enterprise users to view, use, and delete graphs and models. Additionally, we've added two new centrality algorithms for Influence Maximization contributed by a community member, @xkitsios.