Platform For AI:Label Propagation Classification

Algorithm description

When the system runs the label propagation algorithm and classification algorithm, the label of each vertex is propagated to the neighboring vertices based on the similarity. In each propagation step, each vertex updates its label based on the labels of its neighboring vertices. A higher similarity indicates a higher labeling influence that the neighboring vertices have on the vertex. In this case, the labels are easily propagated. During label propagation, the labels of the labeled data remain unchanged. These labels serve as sources for propagation to the unlabeled data. After the iterations end, the probability distributions of similar vertices tend to be similar. These vertices can be classified into the same category. This completes the label propagation.

Configure the component

Method 1: Configure the component on the pipeline page

You can add the Label Propagation Classification component on the pipeline page of Machine Learning Designer in the Platform for AI (PAI) console. The following table describes the parameters.

Method 2: Configure the component by using PAI commands

You can configure the Label Propagation Classification 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 LabelPropagationClassification
    -project algo_public
    -DinputEdgeTableName=LabelPropagationClassification_func_test_edge
    -DfromVertexCol=flow_out_id
    -DtoVertexCol=flow_in_id
    -DinputVertexTableName=LabelPropagationClassification_func_test_node
    -DvertexCol=node
    -DvertexLabelCol=label
    -DoutputTableName=LabelPropagationClassification_func_test_result
    -DhasEdgeWeight=true
    -DedgeWeightCol=edge_weight
    -DhasVertexWeight=true
    -DvertexWeightCol=label_weight
    -Dalpha=0.8
    -Depsilon=0.000001;

Example

1. 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 LabelPropagationClassification_func_test_edge; create table LabelPropagationClassification_func_test_edge as select * from ( select 'a' as flow_out_id, 'b' as flow_in_id, 0.2 as edge_weight union all select 'a' as flow_out_id, 'c' as flow_in_id, 0.8 as edge_weight union all select 'b' as flow_out_id, 'c' as flow_in_id, 1.0 as edge_weight union all select 'd' as flow_out_id, 'b' as flow_in_id, 1.0 as edge_weight )tmp ; drop table if exists LabelPropagationClassification_func_test_node; create table LabelPropagationClassification_func_test_node as select * from ( select 'a' as node,'X' as label, 1.0 as label_weight union all select 'd' as node,'Y' as label, 1.0 as label_weight )tmp;

   Data structure

2. 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 ${o1};
   PAI -name LabelPropagationClassification
       -project algo_public
       -DinputEdgeTableName=LabelPropagationClassification_func_test_edge
       -DfromVertexCol=flow_out_id
       -DtoVertexCol=flow_in_id
       -DinputVertexTableName=LabelPropagationClassification_func_test_node
       -DvertexCol=node
       -DvertexLabelCol=label
       -DoutputTableName=${o1}
       -DhasEdgeWeight=true
       -DedgeWeightCol=edge_weight
       -DhasVertexWeight=true
       -DvertexWeightCol=label_weight
       -Dalpha=0.8
       -Depsilon=0.000001;

3. Right-click the SQL Script component and choose View Data > SQL Script Output to view the training results.

   | node | tag | weight              |
   | ---- | --- | ------------------- |
   | a    | X   | 1.0                 |
   | c    | X   | 0.5370370370370371  |
   | c    | Y   | 0.4629629629629629  |
   | b    | X   | 0.16666666666666666 |
   | b    | Y   | 0.8333333333333333  |
   | d    | Y   | 1.0                 |