List and drop graphs

Now that you've practiced creating various types of projectionsiFull definition for projection (opens in a new tab)An in-memory copy of part of your database that graph algorithms run against. You choose which nodes and relationships it holds.—monopartiteiFull definition for monopartite graph (opens in a new tab)A graph in which every node is the same kind of thing, so relationships connect like to like., bipartiteiFull definition for bipartite graph (opens in a new tab)A graph with two kinds of node, where every relationship joins one kind to the other and never two of the same kind., and multipartiteiFull definition for multipartite graph (opens in a new tab)A graph with three or more kinds of node, where relationships only ever join nodes of different kinds.—it's time to learn how to manage them efficiently.

Throughout this course, you've been creating graph projections using gds.graph.project(). Each projection gets stored in GDSiGo to glossary for graph data science (opens in a new tab)Analysing data through the structure of its connections. Also the name of the Neo4j library that implements it.'s graph catalogiFull definition for graph catalog (opens in a new tab)The set of projections currently held in memory, each listed and dropped by name.—an in-memory store of all your projected graphs.