Modularity is a metric that is used to evaluate the structure of communities in a network. It is designed to measure the strength of division of a network into communities. Values greater than 0.3 indicate a strong community structure. This topic describes the Modularity component provided by Machine Learning Studio.

You can configure the component by using one of the following methods:

Machine Learning Platform for AI console

Tab Parameter Description
Fields Setting Source Vertex Column The start vertex column in the edge table.
Initial Vertex Label Column The group of start vertices in the edge table.
Target Vertex Column The end vertex column in the edge table.
Target Vertex Label Column The group of end vertices in the edge table.
Tuning Workers The number of vertices for parallel job execution. The parallelism level and framework communication costs increase with the value of this parameter.
Memory Size per Worker The maximum size of memory that a single job can use. By default, the system allocates 4,096 MB for each job. If the used memory size exceeds the value of this parameter, the OutOfMemory exception is reported.

PAI command

PAI -name Modularity
    -project algo_public
    -DinputEdgeTableName=Modularity_func_test_edge
    -DfromVertexCol=flow_out_id
    -DfromGroupCol=group_out_id
    -DtoVertexCol=flow_in_id
    -DtoGroupCol=group_in_id
    -DoutputTableName=Modularity_func_test_result;
Parameter Required Description Default value
inputEdgeTableName Yes The name of the input edge table. No default value
inputEdgeTablePartitions No The partitions in the input edge table. Full table
fromVertexCol Yes The start vertex column in the input edge table. No default value
fromGroupCol Yes The group of start vertices in the input edge table. No default value
toVertexCol Yes The end vertex column in the input edge table. No default value
toGroupCol Yes The group of end vertices in the input edge table. No default value
outputTableName Yes The name of the output table. No default value
outputTablePartitions No The partitions in the output table. No default value
lifecycle No The lifecycle of the output table. No default value
workerNum No The number of vertices for parallel job execution. The parallelism level and framework communication costs increase with the value of this parameter. Not configured
workerMem No The maximum size of memory that a single job can use. By default, the system allocates 4,096 MB for each job. If the used memory size exceeds the value of this parameter, the OutOfMemory exception is reported. 4096
splitSize No The data split size. 64

Examples

  1. Generate training data.

    The data is similar to that of the label propagation algorithm. For more information, see .

  2. View training results.
    +--------------+
| val          |
+--------------+
| 0.4230769    |
+--------------+