# apoc.algo

Qualified Name Type

apoc.algo.aStar(startNode, endNode, 'KNOWS|<WORKS_WITH|IS_MANAGER_OF>', 'distance','lat','lon') YIELD path, weight - run A* with relationship property name as cost function

Procedure

apoc.algo.aStar(startNode, endNode, 'KNOWS|<WORKS_WITH|IS_MANAGER_OF>', {weight:'dist',default:10,x:'lon',y:'lat'}) YIELD path, weight - run A* with relationship property name as cost function

Procedure

apoc.algo.allSimplePaths(startNode, endNode, 'KNOWS|<WORKS_WITH|IS_MANAGER_OF>', 5) YIELD path, weight - run allSimplePaths with relationships given and maxNodes

Procedure

apoc.algo.cover(nodes) yield rel - returns all relationships between this set of nodes

Procedure

apoc.algo.dijkstra(startNode, endNode, 'KNOWS|<WORKS_WITH|IS_MANAGER_OF>', 'distance', defaultValue, numberOfWantedResults) YIELD path, weight - run dijkstra with relationship property name as cost function

Procedure

apoc.algo.dijkstraWithDefaultWeight(startNode, endNode, 'KNOWS|<WORKS_WITH|IS_MANAGER_OF>', 'distance', 10) YIELD path, weight - run dijkstra with relationship property name as cost function and a default weight if the property does not exist

Procedure

apoc.algo.cosineSimilarity([vector1], [vector2]) given two collection vectors, calculate cosine similarity

Function

apoc.algo.euclideanDistance([vector1], [vector2]) given two collection vectors, calculate the euclidean distance (square root of the sum of the squared differences)

Function

apoc.algo.euclideanSimilarity([vector1], [vector2]) given two collection vectors, calculate similarity based on euclidean distance

Function