centrality
How important a Full definition for node (opens in a new tab)A vertex in a graph. In a property graph it can carry labels and properties. is within a graph. Each centrality algorithm defines importance differently.
Example
Ben has three relationships. Every route between Eve and the rest runs through Dan.
Degree centrality scores Ben 3 and Dan 2, counting the relationships going out of each. These are undirected, so every relationship goes out of both of its nodes.
Betweenness scores Ben 5 and Dan 3, counting the shortest paths between other people that pass through each.
Lessons that use this term
The lesson and course links below open in a new tab.
27 lessons use this term. Results are ordered by term density.
Understand centrality algorithms30 mentions
- Choose the right familyUnderstand centrality algorithmsthis lab, you have implemented every algorithm in the centrality category, and broken them down into subfamilies, based on the kinds of questions they can1 mention
- ReachUnderstand centrality algorithmsCentrality and Harmonic Centrality are the two algorithms in this10 mentions
- What is centrality?Understand centrality algorithmsCentrality algorithms are often described as finding the "most important" or "most influential"9 mentions
- Explore the graphUnderstand centrality algorithmsCloseness Centrality Harmonic4 mentions
- EndorsementUnderstand centrality algorithmsCentrality, PageRank, Article Rank and HITs are the four algorithms in this4 mentions
- TiesUnderstand centrality algorithmsnow on, you'll start running centrality algorithms against1 mention
- GatekeepingUnderstand centrality algorithmsthe previous lesson Closeness and Harmonic Centrality scored every person by how many steps the rest of the network takes to reach1 mention
Graph Data Science in Practice10 mentions
- Algorithm CategoriesGDS FoundationsCategory | Question | Example Algorithms | | --- | --- | --- | | Centrality | Which nodes are most2 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 Fraud| | 1 | Community Detection | Find groups containing known fraudsters | | 2 | Centrality | Rank users within those groups3 mentions
- Degree Centrality & WCCCommunity Detection for FraudThis is the simplest centrality measure—no iteration, no convergence, just1 mention
Get started with Graph Data Science19 mentions
- Module recapEssential projection techniquesCentrality - Identify important or influential nodes (Degree, PageRank, Betweenness) Community Detection - Find natural3 mentions
- Challenge: Aggregated projection and analysisEssential projection techniquesa community detection or centrality algorithm from the1 mention
- Algorithms overviewWorking with algorithmsalgorithms identify which nodes are most important or influential in a5 mentions
- Running algorithmsWorking with algorithmscounting outgoing relationships to define 'centrality3 mentions
- Module recap and what's nextWorking with algorithmsCentrality - Identify important or influential nodes (Degree, PageRank, Betweenness) Community Detection - Find natural1 mention
- Projection modeling for algorithmsEssential projection techniquesalgorithms (PageRank, Betweenness, Degree) work best1 mention
- Challenge: Projection modeling and analysisEssential projection techniqueswho share actors are connected, and we can use centrality algorithms to measure1 mention
- Practice bipartite projectionsGDS basic concepts| | --- | --- | | Algorithms that expect single-type networks (PageRank, many centrality measures) | Algorithms designed for bipartite structures (Node Similarity) | | You want direct Type-to-Type connections1 mention
- Projecting monopartite graphsGDS basic conceptsYou're using algorithms that expect monopartite structure (PageRank, many centrality measures) The intermediate nodes (like Movies) are just "bridges" for your1 mention
- Practice monopartite projectionsGDS basic conceptskey takeaway here is that 'importance' or 'centrality' only matter in terms of the relationships1 mention
- Understand the five execution modesWorking with algorithmscentrality distribution shows you the range and average number of collaborations across all1 mention
Analyze Graph Data with Python9 mentions
- Scaled Properties and FastRP EmbeddingsGDS Python ClientCentrality and community detection reveal graph5 mentions
- Louvain Community DetectionGDS Python Clientrelate to official subject labels Combine community membership with centrality metrics for richer analysis Interpret community statistics to identify influential and bridging2 mentions
- Introduction to Aura Graph AnalyticsAura Graph Analyticscentrality and community detection algorithms, see the Graph Algorithms1 mention
- Betweenness CentralityGDS Python Clientresults Consider whether you need exact Betweenness or if another centrality measure would1 mention
Aura Graph Analytics fundamentals1 mention
- The AGA workflow end to endFrom the Python clientan Actor-COLLABORATED-Actor graph from the Movies dataset and runs a centrality algorithm on1 mention