1.1 - 8.10 - Graph Analysis - Teradata Vantage

Teradata Vantage™ - Machine Learning Engine Analytic Function Reference

Teradata Vantage
Release Number
October 2019
Content Type
Programming Reference
Publication ID
English (United States)
Function Description
AllPairsShortestPath (ML Engine) Computes the shortest distances between all combinations of the specified source and target vertices.
Betweenness (ML Engine) Determines betweenness for every vertex in a graph. Betweenness is a type of centrality (relative importance) measurement.
Closeness (ML Engine) Computes closeness and k-degree scores for each specified source vertex in a graph.
EigenvectorCentrality (ML Engine) Calculates the centrality (relative importance) of each node in a graph.
GTree (ML Engine) Follows all paths in a graph, starting from a given set of root vertices, and calculates specified aggregate functions along those paths.
LocalClusteringCoefficient (ML Engine) Analyzes the structure of a network.
LoopyBeliefPropagation (ML Engine) Calculates the marginal distribution for each unobserved node, conditional on any observed nodes.
Modularity (ML Engine) Discovers communities (clusters) in input graphs without advance information about the clusters. Detects communities by discovering the strength of relationships among data points.
NTree (ML Engine) Builds and traverses tree structures on all worker nodes in a graph.
PageRank (ML Engine) Computes PageRank values for a directed graph.
PSALSA (ML Engine) Evaluates the similarity of nodes in a bipartite graph according to their proximity. Typically used for recommendation.
RandomWalkSample (ML Engine) Outputs a sample graph that represents the input graph (which is typically extremely large).