PageRank
A centrality algorithm that scores a node by the number of nodes pointing at it and by how important those nodes are.
Example
Two people follow Dan, and Dan follows Eve. Two people follow Fay, and nobody follows either of them.
cypher
CALL gds.pageRank.stream('people')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS person, score
ORDER BY score DESCEve scores highest, on one follower. That follower is Dan, whom two people follow. Fay has two followers of her own and scores exactly what Dan does, because neither Cleo nor Gus brings anything to pass on.
Lessons that use this term
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30 lessons use this term. Results are ordered by term density.
Analyze Graph Data with Python52 mentions
- PageRankGDS Python ClientYou've likely heard of PageRank—it's the original Google algorithm that revolutionized web31 mentions
- Betweenness CentralityGDS Python ClientIn the previous lesson, we used PageRank to find influential papers in our citation12 mentions
- Louvain Community DetectionGDS Python ClientCommunities with high average PageRank contain influential3 mentions
- Workshop Complete!Aura Graph Analyticsprojections to native Python projections Analyzed citation networks with PageRank and Betweenness Centrality Detected research communities with Louvain Generated FastRP embeddings for machine1 mention
- Citation NetworksGDS Python ClientRun PageRank to find influential1 mention
- Scaled Properties and FastRP EmbeddingsGDS Python Clientfrequencies between 0 and 1 Betweenness scores range from 0 to thousands PageRank scores range from 0 to about3 mentions
- Introduction to the Python GDS ClientGDS Python ClientProjecting graphs into memory Running algorithms like PageRank, Betweenness Centrality, Louvain, and FastRP Processing results as DataFrames Cleaning up projections when1 mention
Get started with Graph Data Science92 mentions
- Configuring projections for undirected relationshipsWorking with algorithmsyou'll configure your projection to use undirected relationships and run PageRank and Leiden to see how the results18 mentions
- Practice monopartite projectionsGDS basic conceptsalso measures importance, but weights each node's score based on the importance of its13 mentions
- Configure your algorithmsWorking with algorithmsPageRank with its default settings (empty7 mentions
- Challenge: Projection modeling and analysisEssential projection techniquesPageRank: Measures importance through connections to important nodes ([PageRank6 mentions
- Projecting monopartite graphsGDS basic conceptsPageRank for11 mentions
- Module recap and what's nextWorking with algorithmsCentrality - Identify important or influential nodes (Degree, PageRank, Betweenness) Community Detection - Find natural clusters and groups (Louvain, Leiden, Label Propagation) -3 mentions
- Algorithms overviewWorking with algorithmsprevious module, you already ran several GDS algorithms: degree centrality and PageRank on monopartite graphs, and node similarity on bipartite9 mentions
- Module recapEssential projection techniquesCentrality - Identify important or influential nodes (Degree, PageRank, Betweenness) Community Detection - Find natural clusters and groups (Louvain, Leiden, Label Propagation) -3 mentions
- Projecting bipartite and multipartite graphsGDS basic conceptsalso learned that some algorithms, like PageRank, produce poor results on bipartite structures because the structure itself traps their4 mentions
- Projection modeling for algorithmsEssential projection techniquesalgorithms (PageRank, Betweenness, Degree) work best2 mentions
- Challenge: Monopartite projectionGDS basic conceptstry running PageRank on the same1 mention
- Understand the five execution modesWorking with algorithmsstream PageRank results for the actor collaboration7 mentions
- Module recap and what's nextGDS basic conceptsbasic algorithms (degree centrality, PageRank, node1 mention
- Understand GDS documentationWorking with algorithmsan example for PageRank3 mentions
- Running algorithmsWorking with algorithmsthis lesson, you'll run degree centrality, PageRank and Louvain community detection on the actor collaboration network, experimenting with algorithm-specific2 mentions
- Practice bipartite projectionsGDS basic conceptsprojection | | --- | --- | | Algorithms that expect single-type networks (PageRank, many centrality measures) | Algorithms designed for bipartite structures (Node Similarity) | | You want direct1 mention
- Use GDS utility functionsWorking with algorithmsrun PageRank on the actor network without converting node1 mention
Graph Data Science in Practice17 mentions
- Applying AlgorithmsGDS Foundationsgraphs showing degree, PageRank, and betweenness centrality with nodes of different5 mentions
- Projection PracticeGDS FoundationsRun PageRank on the actor-collab graph in write mode, storing the result as a5 mentions
- Graph Projections and StructureGDS FoundationsPageRank ranks nodes by "importance" based on incoming connections from other important5 mentions
- Algorithm CategoriesGDS Foundations| PageRank, Degree, Betweenness | | Community Detection | What groups1 mention
- Execution Modes and ConfigurationGDS Foundations1 mention
Aura Graph Analytics fundamentals1 mention
- Standalone sessions from DataFramesFrom the Python clientfrom two DataFrames Visualize your projection with neo4j-viz Run PageRank on your projection Write everything back to a1 mention