The Edge Clustering Coefficient is a metric used to measure the extent to which an edge in a network participates in triangle closures within its neighborhood. It is calculated by determining the proportion of triangles formed among the common neighbors of the two nodes connected by the edge. This coefficient is significant in analyzing network structure and community detection, as it helps identify local clustering patterns and group structures.
Configure the component
Method 1: Configure the component on the pipeline page
You can add the Edge Clustering Coefficient component on the pipeline page of Machine Learning Designer in the Platform for AI (PAI) console. The following table describes the parameters.
Tab | Parameter | Description |
Fields Setting | Start Vertex | The start vertex column in the edge table. |
End Vertex | The end vertex column in the edge table. | |
Tuning | Workers | The number of vertices for parallel job execution. The degree of parallelism and framework communication costs increase with the value of this parameter. |
Memory Size per Worker (MB) | The maximum size of memory that a single job can use. Unit: MB. Default value: 4096. If the size of used memory exceeds the value of this parameter, the | |
Data Split Size (MB) | The data split size. Unit: MB. Default value: 64. |
Method 2: Configure the component by using PAI commands
You can configure the Edge Clustering Coefficient component by using PAI commands. You can use the SQL Script component to run PAI commands. For more information, see Scenario 4: Execute PAI commands within the SQL script component in the "SQL Script" topic.
PAI -name EdgeDensity
-project algo_public
-DinputEdgeTableName=EdgeDensity_func_test_edge
-DfromVertexCol=flow_out_id
-DtoVertexCol=flow_in_id
-DoutputTableName=EdgeDensity_func_test_result;Parameter | Required | Default value | Description |
inputEdgeTableName | Yes | No default value | The name of the input edge table. |
inputEdgeTablePartitions | No | Full table | The partitions in the input edge table. |
fromVertexCol | Yes | No default value | The start vertex column in the input edge table. |
toVertexCol | Yes | No default value | The end vertex column in the input edge table. |
outputTableName | Yes | No default value | The name of the output table. |
outputTablePartitions | No | No default value | The partitions in the output table. |
lifecycle | No | No default value | The lifecycle of the output table. |
workerNum | No | No default value | The number of vertices for parallel job execution. The degree of parallelism and framework communication costs increase with the value of this parameter. |
workerMem | No | 4096 | The maximum size of memory that a single job can use. Unit: MB. Default value: 4096. If the size of used memory exceeds the value of this parameter, the |
splitSize | No | 64 | The data split size. Unit: MB. |
Example
Add the SQL Script component as a vertex to the canvas and execute the following SQL statements to generate training data.
drop table if exists EdgeDensity_func_test_edge; create table EdgeDensity_func_test_edge as select * from ( select '1' as flow_out_id,'2' as flow_in_id union all select '1' as flow_out_id,'3' as flow_in_id union all select '1' as flow_out_id,'5' as flow_in_id union all select '1' as flow_out_id,'7' as flow_in_id union all select '2' as flow_out_id,'5' as flow_in_id union all select '2' as flow_out_id,'4' as flow_in_id union all select '2' as flow_out_id,'3' as flow_in_id union all select '3' as flow_out_id,'5' as flow_in_id union all select '3' as flow_out_id,'4' as flow_in_id union all select '4' as flow_out_id,'5' as flow_in_id union all select '4' as flow_out_id,'8' as flow_in_id union all select '5' as flow_out_id,'6' as flow_in_id union all select '5' as flow_out_id,'7' as flow_in_id union all select '5' as flow_out_id,'8' as flow_in_id union all select '7' as flow_out_id,'6' as flow_in_id union all select '6' as flow_out_id,'8' as flow_in_id )tmp; drop table if exists EdgeDensity_func_test_result; create table EdgeDensity_func_test_result ( node1 string, node2 string, node1_edge_cnt bigint, node2_edge_cnt bigint, triangle_cnt bigint, density double );Data structure
Add the SQL Script component as a vertex to the canvas and run the following PAI commands to train the model.
drop table if exists ${o1}; PAI -name EdgeDensity -project algo_public -DinputEdgeTableName=EdgeDensity_func_test_edge -DfromVertexCol=flow_out_id -DtoVertexCol=flow_in_id -DoutputTableName=${o1};Right-click the SQL Script component and choose View Data > SQL Script Output to view the training results.
| node1 | node2 | node1_edge_cnt | node2_edge_cnt | triangle_cnt | density | | ----- | ----- | -------------- | -------------- | ------------ | ------- | | 3 | 1 | 4 | 4 | 2 | 0.5 | | 5 | 1 | 7 | 4 | 3 | 0.75 | | 7 | 1 | 3 | 4 | 1 | 0.33333 | | 1 | 2 | 4 | 4 | 2 | 0.5 | | 4 | 2 | 4 | 4 | 2 | 0.5 | | 2 | 3 | 4 | 4 | 3 | 0.75 | | 5 | 3 | 7 | 4 | 3 | 0.75 | | 3 | 4 | 4 | 4 | 2 | 0.5 | | 8 | 4 | 3 | 4 | 1 | 0.33333 | | 2 | 5 | 4 | 7 | 3 | 0.75 | | 4 | 5 | 4 | 7 | 3 | 0.75 | | 7 | 5 | 3 | 7 | 2 | 0.66667 | | 5 | 6 | 7 | 3 | 2 | 0.66667 | | 8 | 6 | 3 | 3 | 1 | 0.33333 | | 6 | 7 | 3 | 3 | 1 | 0.33333 | | 5 | 8 | 7 | 3 | 2 | 0.66667 |