Algorithms overview

Throughout the previous module, you already ran several 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. algorithms: degree centralityiFull definition for degree centrality (opens in a new tab)A score for each node equal to its number of outgoing relationships. and 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. on monopartite graphsiFull 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., and node similarityiFull definition for node similarity (opens in a new tab)An algorithm that scores how alike two nodes are by comparing the neighbours they share. on bipartite graphsiFull 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..

Each algorithm revealed different insights about the same data.