Projecting bipartite and multipartite graphs

In the previous lessons, you learned that 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. creates unlabelled 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. by default—preserving graph structure but stripping away nodeiFull definition for node (opens in a new tab)A vertex in a graph. In a property graph it can carry labels and properties. labelsiFull definition for label (opens in a new tab)A tag on a node that groups it with other nodes of the same kind. A node can carry more than one. and relationshipiFull definition for relationship (opens in a new tab)A named, directed connection between two nodes. Every relationship has a type, a start node and an end node. types.

You also learned that some algorithms, like PageRankiFull definition for PageRank (opens in a new tab)A centrality algorithm that scores a node by the number of nodes pointing at it and by how important those nodes are., produce poor results on 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. structures because the structure itself traps their computations.