Graph Algorithms

Algorithms

20 built-in graph algorithms across 5 categories, with 19 callable via CALL algo.* and 36 tests. All algorithms execute natively on the storage engine with optional label and relationship type filtering.

Centrality

Community Detection

Pathfinding

Traversal

Similarity

Centrality

Measure the importance or influence of nodes within the graph.

Algorithm	Procedure	Description	Tests
PageRank	algo.pageRank	Iterative link-analysis algorithm ranking nodes by the quantity and quality of incoming links.	2
Betweenness Centrality	algo.betweennessCentrality	Measures how often a node lies on the shortest path between other nodes (Brandes' algorithm).	2
Closeness Centrality	algo.closenessCentrality	Reciprocal of average shortest-path distance to all reachable nodes.	1
Degree Centrality	algo.degreeCentrality	Normalized count of relationships per node (degree / (n-1)).	1

Community Detection

Identify clusters or groups of densely connected nodes.

Algorithm	Procedure	Description	Tests
Louvain	algo.louvain	Modularity optimization algorithm for community detection via greedy local moves.	3
Label Propagation	algo.labelPropagation	Each node adopts the most frequent label among its neighbors until convergence.	1
Weakly Connected Components	algo.weaklyConnectedComponents	Groups nodes connected by any path (ignoring edge direction).	2
Strongly Connected Components	algo.stronglyConnectedComponents	Groups nodes where every node is reachable from every other (Tarjan's algorithm).	3
Triangle Count	algo.triangleCount	Counts triangles and computes local/global clustering coefficients.	1

Pathfinding

Find shortest paths, spanning trees, and topological orderings.

Algorithm	Procedure	Description	Tests
Dijkstra's Shortest Path	algo.shortestPath	Weighted shortest path using a priority queue with optional edge weight property.	2
A* Search	(library only)	Heuristic-guided shortest path with custom heuristic function.	1
All Shortest Paths (BFS)	algo.allShortestPaths	Finds all shortest paths between two nodes using unweighted BFS.	1
K-Hop Neighborhood	algo.kHopNeighborhood	All nodes reachable within k hops from a starting node.	1
Minimum Spanning Tree	algo.minimumSpanningTree	Kruskal's algorithm with union-find for minimum-weight spanning tree.	1
Topological Sort	algo.topologicalSort	Linear ordering of DAG nodes such that every edge goes from earlier to later. Returns empty if cycle detected.	2

Traversal

Systematic graph traversal algorithms.

Algorithm	Procedure	Description	Tests
Breadth-First Search	algo.bfs	Level-by-level traversal from a start node, visiting all reachable nodes.	3
Depth-First Search	algo.dfs	Recursive pre-order traversal from a start node, exploring as deep as possible before backtracking.	2

Similarity

Compute similarity scores between pairs of nodes based on shared neighbors.

Algorithm	Procedure	Description	Tests
Jaccard Similarity	algo.jaccardSimilarity	Ratio of shared neighbors to total neighbors: \|A ∩ B\| / \|A ∪ B\|.	3
Cosine Similarity	algo.cosineSimilarity	Normalized overlap of neighbor sets: \|A ∩ B\| / (sqrt(\|A\|) * sqrt(\|B\|)).	2
Overlap Similarity	algo.overlapSimilarity	Overlap coefficient: \|A ∩ B\| / min(\|A\|, \|B\|).	2

Usage

CALL algo.pageRank('Person', 'KNOWS', {dampingFactor: 0.85, iterations: 20})
YIELD nodeId, score
RETURN algo.asNode(nodeId).name, score
ORDER BY score DESC
LIMIT 10