1.0 - 8.00 - Modularity Arguments - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Teradata Vantage
Release Number
Release Date
May 2019
Content Type
Programming Reference
Publication ID
English (United States)
[Optional] Specify the name of the table that the function creates to output the weights of the edges between the communities at different resolution levels. If a table named community_edge_table exists, the function overwrites the existing table. If you omit this argument, the function does not create this table.
Specify the key of the target vertex of an edge. The key consists of the names of one or more edges table columns.
[Optional] Legacy argument that determined whether the graph was directed. The default value was 'true'. The function ignores this argument, treating all graphs as undirected.
[Optional] Specify the name of the edges table column that contains edge weights. Each edge_weight is a positive value.
Default behavior: The weight of each edge is 1 (that is, the graph is unweighted).

This argument determines how the function treats duplicate edges (that is, edges with the same source and destination, which might have different weights). For a weighted graph, the function treats duplicate edges as a single edge whose weight is the sum of the weights of the duplicate edges. For an unweighted graph, the function uses only one of the duplicate edges.

[Optional] Specify the name of the column that represents the community association of the vertices. Use this argument if you know some vertex communities.
[Optional] Specify hierarchical-level information for the communities. For details, see Resolution. If you specify a list of resolution values, the function incrementally finds the communities for each value and for the default value.
Each resolution must be a distinct DOUBLE PRECISION value in the range [0.0, 1000000.0]. The value 0.0 puts each node in its own community of size 1. You can specify a maximum of 500 resolution values.
Default: 1.0
[Optional] Specify the random seed the algorithm uses for repeatable results (for more information, see Nondeterministic Results). The seed must be a LONG value greater than or equal to 1. The algorithm multiplies seed by the hash code of vertex_key to create a unique seed for each vertex.
Default: 1
The function also uses seed for these purposes:
  • To break ties between different vertices during community formation
  • To determine when to stop community formation

    If seed is 1 (default value), community formation continues until the change in modularity score drops below a threshold value. The threshold value is set in the function code; you cannot change it.

    If seed is set to any other value, community formation continues until the change in modularity score between iterations is 0. This deeper analysis of the graph can improve community formation, but can also increase execution time.

[Optional] Specify the names of the vertices columns to copy to the community vertices table.
Default behavior: The function copies the vertex_key columns to the output vertices table for each vertex, changing the column names to id, id_1, id_2, and so on.