Betweenness Syntax Elements - Teradata Vantage

Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
8.10
1.1
Published
October 2019
Language
English (United States)
Last Update
2019-12-31
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B700-4003
lifecycle
previous
Product Category
Teradata Vantageā„¢
TargetKey
Specify the target key (the names of the Edges table columns that identify the target vertex). If you specify targets_table, then the function uses only the vertices in targets_table as targets (which must be a subset of those that this syntax element specifies).
Directed
[Optional] Specify whether the graph is directed.
Default: 'true'
EdgeWeight
[Optional] Specify the name of the Edges table column that contains edge weights. The weights are positive values.
Default behavior: The weight of each edge is 1 (that is, the graph is unweighted).
MaxDistance
[Optional] Specify the maximum distance (an integer) between the source and target vertices. A negative max_distance specifies an infinite distance. If vertices are separated by more than max_distance, the function does not output them.
Default: 10
GroupSize
[Optional] Specify the number of source vertices that execute a SNSP algorithm in parallel. If group_size exceeds the number of source vertices in each partition, s, then s is the group size.
Default behavior: The function calculates the optimal group size based on various cluster and query characteristics.

Running a group of vertices on each vworker, in parallel, uses less memory than running all vertices on each vworker.

SampleRate
[Optional] Specify the sample rate (the percentage of source vertices to sample), a DOUBLE PRECISION value in the range (0.0, 1.0]. The number of source vertices that the function uses to create betweenness is approximately sample_rate*n, where n is the number of vertices in the graph.
Accumulate
[Optional] Specify the names of the Vertices table columns to copy to the output table. These columns enable you to identify the different betweenness scores in the output table.