GraphAcademy

Graph Data Science

community detection

A family of algorithms that group nodes by how they connect. Each algorithm defines a community differently.

Example

Two groups of three, joined by a single relationship between Cleo and Dan.

Weakly Connected Components returns one community. Every node is reachable from every other.

Louvain returns two. The relationship between Cleo and Dan is the only one crossing between the groups.

Lessons that use this term

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

Graph Data Science in Practice18 mentions

  • Algorithm CategoriesGDS FoundationsPageRank, Degree, Betweenness | | Community Detection | What groups2 mentions
  • Applying AlgorithmsGDS Foundationsthe next module, we'll apply community detection and centrality in detail to a fraud detection use4 mentions
  • Fraud DetectionCommunity Detection for FraudStep | Algorithm Family | Purpose | | --- | --- | --- | | 1 | Community Detection | Find groups containing known fraudsters | | 2 | Centrality | Rank users within those groups4 mentions
  • Workshop Complete!Community Detection for Fraud1 mention
  • Projection Configuration for AlgorithmsGDS Foundationsof unweighted and weighted community detection with nodes and3 mentions
  • Building Fraud CommunitiesCommunity Detection for Fraudas graph relationships Combine Degree Centrality filtering with WCC community detection Identify fraud risk users through guilt-by-association Validate results against transaction2 mentions
  • Graph Projections and StructureGDS FoundationsSimilarity | Designed for bipartite | Preserve labels, include both types | | Community Detection | Varies by algorithm | Check documentation for each1 mention
  • Louvain Community DetectionCommunity Detection for Fraudis a community detection1 mention

Get started with Graph Data Science20 mentions

  • Configuring projections with unweighted relationshipsWorking with algorithmsyou'll create a user-movie bipartite network using rating data and run Leiden community detection with and without relationship4 mentions
  • Module recapEssential projection techniquesIdentify important or influential nodes (Degree, PageRank, Betweenness) Community Detection - Find natural clusters and groups (Louvain, Leiden, Label Propagation) Pathfinding - Discover optimal routes3 mentions
  • Challenge: Aggregated projection and analysisEssential projection techniquesa community detection or centrality algorithm from the1 mention
  • Module recap and what's nextWorking with algorithmsIdentify important or influential nodes (Degree, PageRank, Betweenness) Community Detection - Find natural clusters and groups (Louvain, Leiden, Label Propagation) Pathfinding - Discover optimal routes1 mention
  • Projection modeling for algorithmsEssential projection techniquesdetection (Louvain, Leiden) works best1 mention
  • Algorithms overviewWorking with algorithmsdetection algorithms find natural clusters or groups in your3 mentions
  • Configuring projections for undirected relationshipsWorking with algorithmsis a community detection algorithm that improves on2 mentions
  • Understand GDS documentationWorking with algorithmsif your community detection required retaining the directionality of relationships, you could switch to Louvain, which can run on directed1 mention
  • Use GDS utility functionsWorking with algorithmsLouvain community detection and extract both the actor name and the community they belong1 mention
  • Understand the five execution modesWorking with algorithmsfrom community detection algorithms are particularly important to pay attention2 mentions
  • Running algorithmsWorking with algorithmsthis lesson, you'll run degree centrality, PageRank and Louvain community detection on the actor collaboration network, experimenting with algorithm-specific1 mention

AI on Your Lakehouse: Context Comes in Shapes, Not Queries5 mentions

  • Patterns Nobody NamedSurface Themes - Communitiesthis lesson, you will learn what community detection does and why running it over those cross-reference links produces meaningful3 mentions
  • Port the PatternPort the PatternFundamentals](/courses/genai-fundamentals/) for retrievers and GraphRAG, Community Detection to go deeper on Leiden and1 mention
  • Your EnvironmentThe Context Problema description of the shape you want (plus the neo4j-gds-skill you use for community detection in Module1 mention

Analyze Graph Data with Python4 mentions

Related terms

Louvain algorithm
A community detection algorithm that repeatedly merges nodes into groups for as long as merging raises modularity.19 shared lessons
centrality
How important a node is within a graph. Each centrality algorithm defines importance differently.13 shared lessons
PageRank
A centrality algorithm that scores a node by the number of nodes pointing at it and by how important those nodes are.14 shared lessons
graph data science
Analysing data through the structure of its connections. Also the name of the Neo4j library that implements it.22 shared lessons
pathfinding
A family of algorithms that find routes through a graph. What counts as the best route differs by algorithm.9 shared lessons
undirected relationship
A relationship with no direction. Neo4j GDS represents them as parallel relationships in opposite directions.11 shared lessons
projection
An in-memory copy of part of your database that graph algorithms run against. You choose which nodes and relationships it holds.23 shared lessons
bipartite graph
A graph with two kinds of node, where every relationship joins one kind to the other and never two of the same kind.11 shared lessons

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