文档

Modularity

更新时间:

模块度(Modularity)是一种评估社区网络结构的指标,用来评估社区内部连接相对于社区之间连接的紧密程度,通常模块度为0.3以上表示社区划分质量较为合适。Modularity组件能够输出图的模块度。

配置组件

方法一:可视化方式

在Designer工作流页面添加Modularity组件,并在界面右侧配置相关参数:

参数类型

参数

描述

字段设置

源顶点列

边表的起点所在列。

起始点标签列

边表起点的群组。

目标顶点列

边表的终点所在列。

目标点标签列

边表终点的群组。

执行调优

进程数

作业并行执行的节点数。数字越大并行度越高,但是框架通讯开销会增大。

进程内存

单个作业可使用的最大内存量,单位:MB,默认值为4096。

如果实际使用内存超过该值,会抛出OutOfMemory异常。

方法二:PAI命令方式

使用PAI命令配置Modularity组件参数。您可以使用SQL脚本组件进行PAI命令调用,详情请参见场景4:在SQL脚本组件中执行PAI命令

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;

参数

是否必选

默认值

描述

inputEdgeTableName

输入边表名。

inputEdgeTablePartitions

全表读入

输入边表的分区。

fromVertexCol

输入边表的起点所在列。

fromGroupCol

输入边表起点的群组。

toVertexCol

输入边表的终点所在列。

toGroupCol

输入边表终点的群组。

outputTableName

输出表名。

outputTablePartitions

输出表的分区。

lifecycle

输出表的生命周期。

workerNum

未设置

作业并行执行的节点数。数字越大并行度越高,但是框架通讯开销会增大。

workerMem

4096

单个worker可使用的最大内存量,单位:MB,默认值为4096。

如果实际使用内存超过该值,会抛出OutOfMemory异常。

splitSize

64

数据切分的大小,单位:MB。

使用示例

说明

步骤中SQL脚本组件均去勾选使用Script模式是否由系统添加Create Table语句

  1. 添加SQL脚本组件,输入以下SQL语句生成训练数据。

    drop table if exists Modularity_func_test_edge;
    create table Modularity_func_test_edge as
    select * from
    (
        select '1' as flow_out_id,'3' as group_out_id,'2' as flow_in_id,'3' as group_in_id
        union all
        select '1' as flow_out_id,'3' as group_out_id,'3' as flow_in_id,'3' as group_in_id
        union all
        select '1' as flow_out_id,'3' as group_out_id,'4' as flow_in_id,'3' as group_in_id
        union all
        select '2' as flow_out_id,'3' as group_out_id,'3' as flow_in_id,'3' as group_in_id
        union all
        select '2' as flow_out_id,'3' as group_out_id,'4' as flow_in_id,'3' as group_in_id
        union all
        select '3' as flow_out_id,'3' as group_out_id,'4' as flow_in_id,'3' as group_in_id
        union all
        select '4' as flow_out_id,'3' as group_out_id,'6' as flow_in_id,'7' as group_in_id
        union all
        select '5' as flow_out_id,'7' as group_out_id,'6' as flow_in_id,'7' as group_in_id
        union all
        select '5' as flow_out_id,'7' as group_out_id,'7' as flow_in_id,'7' as group_in_id
        union all
        select '5' as flow_out_id,'7' as group_out_id,'8' as flow_in_id,'7' as group_in_id
        union all
        select '6' as flow_out_id,'7' as group_out_id,'7' as flow_in_id,'7' as group_in_id
        union all
        select '6' as flow_out_id,'7' as group_out_id,'8' as flow_in_id,'7' as group_in_id
        union all
        select '7' as flow_out_id,'7' as group_out_id,'8' as flow_in_id,'7' as group_in_id
    )tmp
    ;

    对应的数据结构图:

    image

  2. 添加SQL脚本组件,输入以下PAI命令进行训练,并和步骤 1中添加的组件连线。

    drop table if exists ${o1};
    PAI -name Modularity
        -project algo_public
        -DinputEdgeTableName=Modularity_func_test_edge
        -DfromVertexCol=flow_out_id
        -DfromGroupCol=group_out_id
        -DtoVertexCol=flow_in_id
        -DtoGroupCol=group_in_id
        -DoutputTableName=${o1};
  3. 运行此工作流。运行完成后右击步骤 2中添加的组件,选择查看数据 > SQL脚本的输出,查看训练结果。

    | val                 |
    | ------------------- |
    | 0.42307692766189575 |