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首页 > 过刊浏览>2019年第28卷第12期 >28-36. DOI:10.15888/j.cnki.csa.007203
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基于融合元路径权重的异质网络表征学习
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Heterogeneous Network Representation Learning Based on Fusion Meta-Path Weights
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    摘要:

    针对基于单条元路径的异质网络表征缺失异质信息网络中结构信息及其它元路径语义信息的问题，本文提出了基于融合元路径权重的异质网络表征学习方法.该方法对异质信息网络中元路径集合进行权重学习，进而对基于不同元路径的低维表征进行加权融合，得到融合不同元路径语义信息的异质网络表征.实验结果表明，基于融合元路径权重的异质网络表征学习具有良好的表征学习能力，可有效应用于数据挖掘.

    Abstract:

    To solve the problem of missing structural information and other meta-path semantic information in heterogeneous network representation based on single meta-path, this study proposes a representation learning method of heterogeneous network based on fusion meta-path weight. This method learns from the set of meta-paths in heterogeneous information networks, and then the low-dimensional representations of different meta-paths are fused with appropriate weights. The representation of heterogeneous networks with semantic information of different meta-paths are obtained. Experiments show that the heterogeneous network representation learning based on fusion meta-path weights has sound representation learning ability and can be effectively applied to data mining.

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蒋宗礼,陈浩强,张津丽.基于融合元路径权重的异质网络表征学习.计算机系统应用,2019,28(12):28-36

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  • 收稿日期:2019-05-28
  • 最后修改日期:2019-06-21
  • 在线发布日期: 2019-12-13
  • 出版日期: 2019-12-15
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