Dynamic Heterogeneous Network Representation Method Based on Meta-Path

LIU Qun; TAN Hong-sheng; ZHANG You-min; WANG Guo-yin

doi:10.12263/DZXB.20211288

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Dynamic Heterogeneous Network Representation Method Based on Meta-Path

PAPERS | 更新时间：2025-12-08

- Dynamic Heterogeneous Network Representation Method Based on Meta-Path
- ACTA ELECTRONICA SINICA Vol. 50, Issue 8, Pages: 1830-1839(2022)
- 作者机构：
  
  重庆邮电大学计算机科学与技术学院，重庆 400065
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20211288
  CLC： TP391
- Received：17 September 2021，
  
  Revised：2022-04-21，
  
  Published：25 August 2022
- 稿件说明：
移动端阅览
刘群,谭洪胜,张优敏等.基于元路径的动态异质网络表示学习[J].电子学报,2022,50(08):1830-1839.

LIU Qun,TAN Hong-sheng,ZHANG You-min,et al.Dynamic Heterogeneous Network Representation Method Based on Meta-Path[J].ACTA ELECTRONICA SINICA,2022,50(08):1830-1839.
刘群,谭洪胜,张优敏等.基于元路径的动态异质网络表示学习[J].电子学报,2022,50(08):1830-1839. DOI： 10.12263/DZXB.20211288.

LIU Qun,TAN Hong-sheng,ZHANG You-min,et al.Dynamic Heterogeneous Network Representation Method Based on Meta-Path[J].ACTA ELECTRONICA SINICA,2022,50(08):1830-1839. DOI： 10.12263/DZXB.20211288.

摘要

对网络表示学习的研究已经取得了很多成果，但是大部分网络表示学习模型忽略了网络动态性和异质性，无法区分网络中耦合的时间和空间（结构）特征，也不能捕获网络的丰富语义信息.本文提出了基于元路径的动态异质网络表示学习方法.首先将节点的邻域结构按照时间划分出不同的子空间结构，并为每个节点采样出所有时间加权元路径的序列.其次通过门控循环单元将节点的全部时间加权元路径序列上的邻域信息进行集成，最后利用带注意力机制的双向门控循环单元对融合后的节点序列进行时空上下文信息学习，获得每个节点的最终表示向量.通过在真实数据集上的实验表明，在节点分类、聚类和可视化的下游任务测试中，本文提出的算法较基线方法在性能上均有较大提升.节点分类任务中的Micro-F1平均提高了1.09%~3.72%，节点聚类任务中的ARI值提高了3.23%～14.49%.

Abstract

The researches of network representation learning have made many achievements. Since most of the researches ignore the dynamics and heterogeneity of the networks

coupled temporal and spatial structure features can not be distinguished

and rich semantic information of the network cannot be captured well. In this paper

meta-path based dynamic heterogeneous network representation learning method is proposed. Firstly

the neighborhood structures of nodes are divided into different sub-spaces according to their time

then the sequences of all time-weighted meta-paths for each node are sampled. Secondly

the neighborhood information on all time-weighted meta-paths of each node is integrated by a gated recurrent unit network(GRU). Furthermore

a bi-directional gated recurrent unit network(Bi-GRU) with an attention mechanism is used to learn the spatio-temporal contextual information from the merged sequences

and the final node representation will be received. Experiments on real data sets show that our algorithm has greatly improved performance on the downstream network tasks

such as node classification

clustering and visualization. Compared with state-of-the-art baseline methods

the Micro-F1 value has been raised by 1.09%~3.72% averagely on classification tasks

and the ARI value has been increased by 3.23%~14.49% on clustering tasks.

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references

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