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  • Introduction
  • Running Algorithms
    • Degree Centrality
    • Closeness Centrality
    • Harmonic Centrality
    • Eccentricity Centrality
    • Betweenness Centrality
    • Bridges
    • Articulation Points
    • Eigenvector Centrality
    • Katz Centrality
    • CELF
    • PageRank
    • ArticleRank
    • TextRank
    • HITS
    • SybilRank
    • Jaccard Similarity
    • Overlap Similarity
    • Cosine Similarity
    • Pearson Correlation Coefficient
    • Euclidean Distance
    • KNN
    • Vector Similarity
    • Bipartite Graph
    • HyperANF
    • Weakly Connected Components (WCC)
    • Strongly Connected Components (SCC)
    • k-Edge Connected Components
    • Local Clustering Coefficient
    • Triangle Count
    • Clique Count
    • k-Core
    • k-Truss
    • p-Cohesion
    • Induced Subgraph
    • Topological Sort
    • Breadth-First Search (BFS)
    • Depth-First Search (DFS)
    • Dijkstra's Shortest Path
    • A* Shortest Path
    • Yen's K-Shortest Paths
    • Shortest Path (BFS)
    • Delta-Stepping SSSP
    • Shortest Path Faster Algorithm (SPFA)
    • All-Pairs Shortest Path (APSP)
    • Minimum Spanning Tree (MST)
    • K-Spanning Tree
    • Steiner Tree
    • Prize-Collecting Steiner Tree (PCST)
    • Minimum Cost Flow
    • Maximum Flow
    • K-Hop Fast
    • Longest Path (DAG)
    • Random Walk
    • Adamic-Adar Index
    • Common Neighbors
    • Preferential Attachment
    • Resource Allocation
    • Total Neighbors
    • Same Community
    • Louvain
    • Leiden
    • Modularity Optimization
    • Label Propagation
    • HANP
    • SLPA
    • k-Means
    • HDBSCAN
    • K-1 Coloring
    • Modularity
    • Conductance
    • Max k-Cut
      • Node2Vec
      • Struc2Vec
      • LINE
      • Fast Random Projection
      • Summary of Graph Embedding
      • Gradient Descent
      • Backpropagation
      • Skip-gram
      • Skip-gram Optimization
  1. Docs
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  3. Graph Algorithms
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  5. Centrality

Articulation Points

Overview

The Articulation Points algorithm finds cut vertices in a graph — nodes whose removal would disconnect the graph (or increase the number of connected components). Articulation points represent critical nodes and potential vulnerabilities in a network.

Concepts

Articulation Point

An articulation point (also called a cut vertex) is a node in an undirected graph whose removal, along with all its incident edges, increases the number of connected components. Removing an articulation point splits a connected part of the graph into two or more separate parts.

Articulation point detection is closely related to bridge detection. A bridge edge always connects to at least one articulation point (unless the bridge connects two leaf nodes). However, an articulation point does not necessarily have a bridge edge.

Articulation point detection is important for:

  • Network reliability: Identifying single points of failure in infrastructure or communication networks.
  • Graph structure analysis: Finding the biconnected components of a graph.
  • Critical node protection: Prioritizing the protection or redundancy of critical nodes.

Considerations

  • The algorithm treats all edges as undirected.
  • Isolated nodes are not articulation points.

Example Graph

GQL
INSERT (A:default {_id: "A"}), (B:default {_id: "B"}),
       (C:default {_id: "C"}), (D:default {_id: "D"}),
       (E:default {_id: "E"}), (F:default {_id: "F"}),
       (A)-[:default]->(B), (B)-[:default]->(C),
       (C)-[:default]->(A), (C)-[:default]->(D),
       (D)-[:default]->(E), (E)-[:default]->(F),
       (F)-[:default]->(D)

Run Mode

Returns:

ColumnTypeDescription
nodeIdSTRINGNode identifier (_id)
isCutVertexBOOLWhether the node is an articulation point

Find all articulation points:

GQL
CALL algo.articulationpoints() YIELD nodeId, isCutVertex

Result:

nodeIdisCutVertex
Dtrue
Ctrue

Stream Mode

Returns the same columns as run mode, streamed for memory efficiency.

GQL
CALL algo.articulationpoints.stream() YIELD nodeId, isCutVertex
RETURN collect_list(nodeId)

Result:

collect_list(nodeId)
["D", "C"]

Stats Mode

Returns:

ColumnTypeDescription
nodeCountINTTotal number of nodes
cutVertexCountINTNumber of articulation points
GQL
CALL algo.articulationpoints.stats() YIELD nodeCount, cutVertexCount

Result:

nodeCountcutVertexCount
62

Write Mode

Computes results and writes them back to node properties. The write configuration is passed as a second argument map.

Write parameters:

NameTypeDescription
db.propertySTRING or MAPNode property to write results to. String: writes the isCutVertex column in results to a property. Map: explicit column-to-property mapping (e.g., {isCutVertex: 'is_cut'}).

Writable columns:

ColumnTypeDescription
isCutVertexBOOLWhether the node is an articulation point

Returns:

ColumnTypeDescription
task_idSTRINGTask identifier for tracking via SHOW TASKS
nodesWrittenINTNumber of nodes with properties written
computeTimeMsINTTime spent computing the algorithm (milliseconds)
writeTimeMsINTTime spent writing properties to storage (milliseconds)
GQL
CALL algo.articulationpoints.write({}, {
  db: {
    property: "is_cut"
  }
}) YIELD task_id, nodesWritten, computeTimeMs, writeTimeMs