Graph Data Science

FastRP

A node embedding algorithm, short for Fast Random Projection. It builds each node's vector by combining random vectors drawn from the nodes around it.

Example

A vector of four numbers for each node in the projection.

cypher
CALL gds.fastRP.stream('people', {
  embeddingDimension: 4,
  randomSeed: 42
})
YIELD nodeId, embedding
RETURN gds.util.asNode(nodeId).name AS person, embedding

embeddingDimension sets how many numbers each vector holds. randomSeed fixes the random vectors the algorithm starts from, which would otherwise differ between runs.

Lessons that use this term

The lesson and course links below open in a new tab.

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

Analyze Graph Data with Python20 mentions

  • Scaled Properties and FastRP EmbeddingsGDS Python Clientthis lesson, you'll scale features, create embeddings with FastRP, and cluster papers to compare with official subject18 mentions
  • Workshop Complete!Aura Graph AnalyticsCentrality Detected research communities with Louvain Generated FastRP embeddings for machine learning1 mention
  • Introduction to the Python GDS ClientGDS Python ClientRunning algorithms like PageRank, Betweenness Centrality, Louvain, and FastRP Processing results as DataFrames Cleaning up projections when we're1 mention

Get started with Graph Data Science8 mentions

  • Module recap and what's nextWorking with algorithmsSimilarity - Find structurally similar nodes (Node Similarity, FastRP) Embeddings - Create vector representations of nodes (FastRP,2 mentions
  • Module recapEssential projection techniquesSimilarity - Find structurally similar nodes (Node Similarity, FastRP) Embeddings - Create vector representations of nodes (FastRP,2 mentions
  • Algorithms overviewWorking with algorithms(Fast Random Projection) creates node embeddings that capture both local and global network4 mentions

Graph Data Science in Practice5 mentions

Aura Graph Analytics fundamentals2 mentions

All glossary terms