* 社交网络的表示学习任务 <https://blog.csdn.net/liudingbobo/article/details/83039233#_2>
* 异构图的网络表示 <https://blog.csdn.net/liudingbobo/article/details/83039233#_6>
* 论文1：在异构的社交网络中学习节点的潜在表示[^10]
<https://blog.csdn.net/liudingbobo/article/details/83039233#110_17>
* 论文2：应用于社交网络的异构图形的分类学习[^11]
<https://blog.csdn.net/liudingbobo/article/details/83039233#211_20>
* reference
<https://blog.csdn.net/liudingbobo/article/details/83039233#reference_24>

<>社交网络的表示学习任务

1 <https://blog.csdn.net/liudingbobo/article/details/83039233#fn1>。

<>异构图的网络表示

* 将异构图映射到同构图2 <https://blog.csdn.net/liudingbobo/article/details/83039233#fn2>
3 <https://blog.csdn.net/liudingbobo/article/details/83039233#fn3>4
<https://blog.csdn.net/liudingbobo/article/details/83039233#fn4>5
<https://blog.csdn.net/liudingbobo/article/details/83039233#fn5>
，但是这种方法没有完全的探索不同节点之间，或者他们标签之间的联系，甚至提出了不太实际的假设1
<https://blog.csdn.net/liudingbobo/article/details/83039233#fn1>，所以准确率较低。
* 对不同类型的节点使用不同类型的编码6
<https://blog.csdn.net/liudingbobo/article/details/83039233#fn6>。
* 用特定类型的参数来扩展成对的解码器(这句话我现在也不知道什么意思，求赐教)7
<https://blog.csdn.net/liudingbobo/article/details/83039233#fn7>8
<https://blog.csdn.net/liudingbobo/article/details/83039233#fn8>。
* 还有一种是对random walks的扩展9
<https://blog.csdn.net/liudingbobo/article/details/83039233#fn9>。

<>论文1：在异构的社交网络中学习节点的潜在表示10
<https://blog.csdn.net/liudingbobo/article/details/83039233#fn10>

<>论文2：应用于社交网络的异构图形的分类学习11
<https://blog.csdn.net/liudingbobo/article/details/83039233#fn11>

<>reference

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