GraphAcademy

Graph Data Science

weakly connected components

Groups of nodes in which every node is reachable from every other, once relationship direction is ignored. The GDS algorithm that finds them takes the same name.

Also written: WCC

Example

Five people in two groups, with no relationship running between the groups.

cypher
CALL gds.wcc.stream('people')
YIELD nodeId, componentId
RETURN gds.util.asNode(nodeId).name AS person, componentId

Ava, Ben and Cleo come back with one componentId. Dan and Eve come back with another. No relationship joins the two groups, so nothing puts all five in one component.

Lessons that use this term

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7 lessons use this term. Results are ordered by term density.

Graph Data Science in Practice43 mentions

  • Degree Centrality & WCCCommunity Detection for Fraudof Louvain, Degree Centrality, and WCC algorithms with labeled26 mentions
  • Building Fraud CommunitiesCommunity Detection for Fraudthat encode fraud hypotheses, use Degree Centrality to filter noise, and run WCC to identify communities of connected11 mentions
  • Algorithm CategoriesGDS FoundationsLouvain, Leiden, WCC | | Similarity | Which nodes are1 mention
  • Workshop Complete!Community Detection for FraudUsed Louvain to reduce a 204,000-user search space by 98% Applied WCC for deterministic, explainable community assignment Filtered noise with Degree Centrality Encoded domain hypotheses1 mention
  • Reducing Search Space with LouvainCommunity Detection for FraudCentrality — Identify high-connection nodes (potential hubs or noise) Weakly Connected Components (WCC) — Deterministic community assignment for auditable2 mentions
  • Fraud DetectionCommunity Detection for Fraudto reduce your search space by 98% Lesson 4: Learn Degree Centrality and WCC for formal community assignment Lesson 5: Build fraud communities using entity resolution1 mention
  • Louvain Community DetectionCommunity Detection for FraudLesson 5, you'll learn how to use WCC for deterministic, explainable community1 mention

Related terms

Louvain algorithm
A community detection algorithm that repeatedly merges nodes into groups for as long as merging raises modularity.7 shared lessons
modularity
A score for how much more densely connected the nodes within a group are than they would be by chance.3 shared lessons
undirected relationship
A relationship with no direction. Neo4j GDS represents them as parallel relationships in opposite directions.5 shared lessons
degree centrality
A score for each node equal to its number of outgoing relationships.5 shared lessons
community detection
A family of algorithms that group nodes by how they connect. Each algorithm defines a community differently.5 shared lessons
heterogeneous graph
A graph that holds more than one node label or more than one relationship type.2 shared lessons
Union-Find
The algorithm underlying Weakly Connected Components. It holds each node in a set, and merges two sets whenever a relationship joins them.1 shared lesson
centrality
How important a node is within a graph. Each centrality algorithm defines importance differently.3 shared lessons

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