GraphAcademy

Graph Data Science

degree centrality

A score for each node equal to its number of outgoing relationships.

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

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

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

Related terms

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.14 shared lessons
centrality
How important a node is within a graph. Each centrality algorithm defines importance differently.11 shared lessons
Louvain algorithm
A community detection algorithm that repeatedly merges nodes into groups for as long as merging raises modularity.12 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.13 shared lessons
monopartite graph
A graph in which every node is the same kind of thing, so relationships connect like to like.10 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
community detection
A family of algorithms that group nodes by how they connect. Each algorithm defines a community differently.10 shared lessons
graph data science
Analysing data through the structure of its connections. Also the name of the Neo4j library that implements it.16 shared lessons

All glossary terms