apoc.algo.cosineSimilarity([vector1], [vector2])

Compute cosine similarity

apoc.algo.euclideanDistance([vector1], [vector2])

Compute Euclidean distance

apoc.algo.euclideanSimilarity([vector1], [vector2])

Compute Euclidean similarity

apoc.algo.betweenness(['TYPE',…​],nodes,BOTH) YIELD node, score

calculate betweenness centrality for given nodes

apoc.algo.closeness(['TYPE',…​],nodes, INCOMING) YIELD node, score

calculate closeness centrality for given nodes

apoc.algo.cover(nodeIds) YIELD rel

return relationships between this set of nodes

apoc.algo.pageRank(nodes) YIELD node, score

calculates page rank for given nodes

apoc.algo.pageRankWithConfig(nodes,{iterations:_,types:_}) YIELD node, score

calculates page rank for given nodes

apoc.algo.community(times,labels,partitionKey,type,direction,weightKey,batchSize)

simple label propagation kernel

apoc.algo.cliques(minSize) YIELD clique

search the graph and return all maximal cliques at least at large as the minimum size argument.

apoc.algo.cliquesWithNode(startNode, minSize) YIELD clique

search the graph and return all maximal cliques that are at least as large than the minimum size argument and contain this node