What is degree centrality?

Example

Ava follows three people. Two people follow Cleo, who follows nobody.

cypher

CALL gds.degree.stream('people')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS person, score
ORDER BY score DESC

Ava scores 3 and Ben 1. Cleo scores 0, even though two of the four relationships arrive there, because a relationship arriving at a node is not counted. Passing orientation: 'REVERSE' to the algorithm counts those instead, and scores Cleo 2.

Lessons that use this term

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

Understand centrality algorithms16 mentions

TiesUnderstand centrality algorithmsCentrality is the only algorithm in this10 mentions
Explore the graphUnderstand centrality algorithmsCentrality ranks Person 323 above Person 400, because 323 sent more2 mentions
EndorsementUnderstand centrality algorithmswhen you ran Degree Centrality, Person 1624 did not appear in the top rankings at2 mentions
ReachUnderstand centrality algorithmspeople have only two direct contacts, so Degree Centrality gives them the same low1 mention
What is centrality?Understand centrality algorithmsGraph Data Science library offers Degree Centrality as the only member of this1 mention

Get started with Graph Data Science54 mentions

Challenge: Projection modeling and analysisEssential projection techniquesDegree Centrality: Counts direct connections ([Degree centrality5 mentions
Practice monopartite projectionsGDS basic conceptsdegree centrality on the 'movies-only' graph to find movies with the most connections to other movies (through shared9 mentions
Understand the five execution modesWorking with algorithmsdegree centrality in stats mode to see the distribution of collaboration11 mentions
Running algorithmsWorking with algorithmsthis lesson, you'll run degree centrality, PageRank and Louvain community detection on the actor collaboration network, experimenting with algorithm-specific8 mentions
Algorithms overviewWorking with algorithmsthe previous module, you already ran several GDS algorithms: degree centrality and PageRank on monopartite graphs, and node similarity on bipartite8 mentions
Challenge: Monopartite projectionGDS basic conceptsyou've created your projection, run degree centrality to find actors who've worked with many directors who've also worked with many other1 mention
Understand the GDS workflowGDS basic conceptsexample, the degree centrality algorithm performs a relatively simple operation: it assigns a score to each node by counting the number of outgoing4 mentions
Module recap and what's nextGDS basic conceptsbasic algorithms (degree centrality, PageRank, node1 mention
Projection modeling for algorithmsEssential projection techniquesinferred relationship projection or graph refactor) Algorithm: PageRank, Degree Centrality or Betweenness could all frame the same answer in slightly different1 mention
Projecting monopartite graphsGDS basic conceptswe ran degree centrality on this2 mentions
Configuring projections for undirected relationshipsWorking with algorithmsshould be identical —same as with degree centrality2 mentions
Configure your algorithmsWorking with algorithmsthe next lesson, you'll practice running degree centrality in all four execution modes with different1 mention
Projecting bipartite and multipartite graphsGDS basic conceptsalgorithm ignores types entirely — Degree centrality just counts1 mention

Graph Data Science in Practice21 mentions

Degree Centrality & WCCCommunity Detection for Fraudof Louvain, Degree Centrality, and WCC algorithms with labeled8 mentions
Projection Configuration for AlgorithmsGDS FoundationsNow let's run degree centrality on both directed and undirected projections and compare the4 mentions
Workshop Complete!Community Detection for FraudWCC for deterministic, explainable community assignment Filtered noise with Degree Centrality Encoded domain hypotheses as graph relationships Identified 211 previously unknown fraud risk users handling 13% of1 mention
Applying AlgorithmsGDS FoundationsCentrality counts outgoing2 mentions
Building Fraud CommunitiesCommunity Detection for Fraudthis lesson, you'll create relationships that encode fraud hypotheses, use Degree Centrality to filter noise, and run WCC to identify communities of connected4 mentions
Reducing Search Space with LouvainCommunity Detection for FraudDegree Centrality — Identify high-connection nodes (potential hubs or noise) Weakly Connected Components (WCC) — Deterministic1 mention
Fraud DetectionCommunity Detection for Fraud3: Run Louvain to 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

Analyze Graph Data with Python1 mention

GDS Workflows in PythonGDS Python Clientexample, to run degree centrality in mutate1 mention

Aura Graph Analytics fundamentals2 mentions

Aura Graph Analytics workflowAura Graph Analytics in the Aura Workspacethe command below to run degree centrality again, and then write the number of unique collaborations as a property to every actor in the1 mention
The AGA workflow end to endFrom the Python clientwalk through stream, mutate, and write against the same algorithm: degree centrality1 mention

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