FastRP
A Full definition for node embedding (opens in a new tab)A list of numbers that stands in for a node's position in the graph, so that nodes in similar positions get similar lists. algorithm, short for Fast Random Full definition for projection (opens in a new tab)An in-memory copy of part of your database that graph algorithms run against. You choose which nodes and relationships it holds.. It builds each Full definition for node (opens in a new tab)A vertex in a graph. In a property graph it can carry labels and properties.'s vector by combining random vectors drawn from the nodes around it.
Example
A vector of four numbers for each node in the projection.
CALL gds.fastRP.stream('people', {
embeddingDimension: 4,
randomSeed: 42
})
YIELD nodeId, embedding
RETURN gds.util.asNode(nodeId).name AS person, embeddingembeddingDimension 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
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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
- Algorithm CategoriesGDS Foundations| FastRP, Node2Vec1 mention
- Applying AlgorithmsGDS FoundationsFastRP creates vector representations capturing each node's network2 mentions
- Projection Configuration for AlgorithmsGDS Foundationsparameter tells FastRP to incorporate those node attributes into the2 mentions
Aura Graph Analytics fundamentals2 mentions
- Train and publish machine learning modelsAura Graph Analytics in the Aura Workspacegive every node a meaningful per-node feature vector, mutate a FastRP embedding onto the2 mentions