Java 类名:com.alibaba.alink.operator.batch.graph.MetaPath2VecBatchOp
Python 类名:MetaPath2VecBatchOp
沿着之前random walk的思路往前走,metapath2vec的方法提出了控制随机游走的模式,这样就可以在生成的序列上根据节点类型的不同来控制序列游走,这样也就可以对异质网络(Heterogeneous Networks)进行表征学习。在游走之前需要设定一个metapath,也就是游走时节点类型的模式
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
| 名称 | 中文名称 | 描述 | 类型 | 是否必须? | 取值范围 | 默认值 | 
|---|---|---|---|---|---|---|
| metaPath | 游走的模式 | 一般为用字符串表示,例如 “ABDFA” | String | ✓ | ||
| sourceCol | 起始点列名 | 用来指定起始点列 | String | ✓ | ||
| targetCol | 中止点点列名 | 用来指定中止点列 | String | ✓ | ||
| typeCol | 节点类型列名 | 用来指定节点类型列 | String | ✓ | ||
| vertexCol | 节点列名 | 用来指定节点列 | String | ✓ | 所选列类型为 [STRING] | |
| walkLength | 游走的长度 | 随机游走完向量的长度 | Integer | ✓ | ||
| walkNum | 路径数目 | 每一个起始点游走出多少条路径 | Integer | ✓ | ||
| alpha | 学习率 | 学习率 | Double | 0.025 | ||
| batchSize | batch大小 | batch大小, 按行计算 | Integer | x >= 1 | ||
| isToUndigraph | 是否转无向图 | 选为true时,会将当前图转成无向图,然后再游走 | Boolean | false | ||
| minCount | 最小词频 | 最小词频 | Integer | 5 | ||
| mode | metapath中word2vec的模式,分别为metapath2vec和metapath2vecpp | metapath的模式 | String | “METAPATH2VEC”, “METAPATH2VECPP” | “METAPATH2VEC” | |
| negative | 负采样大小 | 负采样大小 | Integer | 5 | ||
| numIter | 迭代次数 | 迭代次数,默认为1。 | Integer | 1 | ||
| randomWindow | 是否使用随机窗口 | 是否使用随机窗口,默认使用 | String | “true” | ||
| vectorSize | embedding的向量长度 | embedding的向量长度 | Integer | x >= 1 | 100 | |
| weightCol | 权重列名 | 权重列对应的列名 | String | 所选列类型为 [BIGDECIMAL, BIGINTEGER, BYTE, DOUBLE, FLOAT, INTEGER, LONG, SHORT] | null | |
| window | 窗口大小 | 窗口大小 | Integer | 5 | 
from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
    ["Bob", "Lucy", 1.],
    ["Lucy", "Bob", 1.],
    ["Lucy", "Bella", 1.],
    ["Bella", "Lucy", 1.],
    ["Alice", "Lisa", 1.],
    ["Lisa", "Alice", 1.],
    ["Lisa", "Karry", 1.],
    ["Karry", "Lisa", 1.],
    ["Karry", "Bella", 1.],
    ["Bella", "Karry", 1.]
])
source = BatchOperator.fromDataframe(df, schemaStr="start string, end string, value double")
df2 = pd.DataFrame([
    ["Bob", "A"],
    ["Bella", "A"],
    ["Karry", "A"],
    ["Lucy", "B"],
    ["Alice", "B"],
    ["Lisa", "B"],
    ["Karry", "B"]
])
type = BatchOperator.fromDataframe(df2, schemaStr="node string, type string")
metapathBatchOp = MetaPath2VecBatchOp() \
    .setSourceCol("start") \
    .setTargetCol("end") \
    .setWeightCol("value") \
    .setVertexCol("node") \
    .setTypeCol("type") \
    .setMetaPath("ABA") \
    .setWalkNum(2) \
    .setWalkLength(2) \
    .setMinCount(1) \
    .setVectorSize(4)
metapathBatchOp.linkFrom(source, type).print()
| node | vec | 
|---|---|
| Karry | -0.028718041256070137,0.02825581468641758,0.12125638127326965,0.1207452341914177 | 
| Bella | 0.03437831997871399,-0.0477546751499176,0.012570690363645554,-0.0958133116364479 | 
| Bob | 0.024427175521850586,0.07044785469770432,-0.04175269603729248,-0.06182029843330383 | 
| Lucy | 0.05776885524392128,0.08288335055112839,-0.06490718573331833,0.026563744992017746 |