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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
  2. /
  3. Graph Algorithms
  4. /
  5. Link Prediction

Same Community

Overview

The Same Community algorithm checks whether two nodes belong to the same weakly connected component. It is a simple link prediction indicator — nodes in the same component are more likely to be connected or have a relationship.

Considerations

  • The algorithm treats all edges as undirected (weakly connected components).

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

Parameters

NameTypeDefaultDescription
node1STRING/Required. First node _id.
node2STRING/Required. Second node _id.

Run Mode

Returns:

ColumnTypeDescription
node1STRINGFirst node identifier (_id)
node2STRINGSecond node identifier (_id)
sameCommunityBOOLWhether the two nodes are in the same community
GQL
CALL algo.samecommunity({
  node1: "A",
  node2: "G"
}) YIELD node1, node2, sameCommunity

Result:

node1node2sameCommunity
AGfalse

Stream Mode

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

GQL
CALL algo.samecommunity.stream({
  node1: "A",
  node2: "G"
}) YIELD node1, node2, sameCommunity
RETURN node1, node2, sameCommunity

Result:

node1node2sameCommunity
AGfalse

Stats Mode

Returns:

ColumnTypeDescription
sameCommunityBOOLWhether the two nodes are in the same community
GQL
CALL algo.samecommunity.stats({
  node1: "A",
  node2: "G"
}) YIELD sameCommunity

Result:

sameCommunity
false