Meta-path convolution based heterogeneous graph neural network algorithm

QIN Zhilong; DENG Kun; LIU Xingyan

doi:10.11959/j.issn.1000-0801.2024078

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Meta-path convolution based heterogeneous graph neural network algorithm

Research and Development | 更新时间：2024-06-05

- Meta-path convolution based heterogeneous graph neural network algorithm
- Telecommunications Science Vol. 40, Issue 3, Pages: 89-103(2024)
- 作者机构：
  
  1.嘉兴大学信息科学与工程学院，浙江嘉兴 314001
  2.浙江理工大学计算机科学与技术学院（人工智能学院），浙江杭州 310018
  3.嘉兴大学浙江省全省多模态感知与智能系统重点实验室，浙江嘉兴 314001
- 作者简介：
- 基金信息：
  
  The Humanity and Social Science Research Project of Ministry of Education of China(22JDSZ3023);The Ministry of Education's Industry-University Collaboration Education Project(220603372015422;220604029012441)
- DOI：10.11959/j.issn.1000-0801.2024078
  CLC： TP391
- Received：08 January 2024，
  
  Revised：15 March 2024，
  
  Published：20 March 2024
- 稿件说明：
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秦志龙,邓琨,刘星妍.基于元路径卷积的异构图神经网络算法[J].电信科学,2024,40(03):89-103.

QIN Zhilong,DENG Kun,LIU Xingyan.Meta-path convolution based heterogeneous graph neural network algorithm[J].Telecommunications Science,2024,40(03):89-103.
秦志龙,邓琨,刘星妍.基于元路径卷积的异构图神经网络算法[J].电信科学,2024,40(03):89-103. DOI： 10.11959/j.issn.1000-0801.2024078.

QIN Zhilong,DENG Kun,LIU Xingyan.Meta-path convolution based heterogeneous graph neural network algorithm[J].Telecommunications Science,2024,40(03):89-103. DOI： 10.11959/j.issn.1000-0801.2024078.

摘要

现有异构图嵌入方法在多层图卷积计算中，通常将每个节点表示为单个向量，使得高阶图卷积层无法区分不同关系和顺序的信息，导致信息在传递过程中丢失。为解决该问题，提出了基于元路径卷积的异构图神经网络算法。该方法首先利用特征转换自适应调整节点特征；其次，设计了元路径内卷积挖掘节点高阶间接关系，捕获目标节点在单元路径下与其他类型节点之间的交互关系；最后，通过自注意力机制探索语义之间的相互性，融合来自不同元路径的特征。在ACM、IMDB和DBLP数据集上进行广泛实验，并与当前主流算法进行对比分析。实验结果显示，节点分类任务中Macro-F1平均提高0.5%～3.5%，节点聚类任务中ARI值提高了1%～3%，证明该算法是有效、可行的。

Abstract

In the multilayer graph convolution calculation

each node is usually represented as a single vector

which makes the high-order graph convolution layer unable to distinguish the information of different relationships and sequences

resulting in the loss of information in the transmission process. To solve this problem

a heterogeneous graph neural network algorithm based on meta-path convolution was proposed. Firstly

the feature transformation was used to adaptively adjust the node features. Secondly

the high-order indirect relationship between the nodes was mined by convolution within the meta-path to capture the interaction between the target node and other types of nodes under the element path. Finally

the reciprocity between semantics was explored through the self-attention mechanism

and the features from different meta-paths were fused. Extensive experiments were carried out on ACM

IMDB and DBLP datasets

and compared with the current mainstream algorithms. The experimental results show that the average increase of Macro-F1 in the node classification task is 0.5%～3.5%

and the ARI value in the node clustering task is increased by 1%～3%

which proves that the algorithm is effective and feasible.

关键词

Keywords

references

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