基于元路径卷积的异构图神经网络算法

秦志龙; 邓琨; 刘星妍

doi:10.11959/j.issn.1000-0801.2024078

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基于元路径卷积的异构图神经网络算法

研究与开发 | 更新时间：2024-06-05

- 基于元路径卷积的异构图神经网络算法
- Meta-path convolution based heterogeneous graph neural network algorithm
- 电信科学 2024年40卷第3期页码：89-103
- 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：
  
  [ "秦志龙（1999- ），男，浙江理工大学计算机科学与技术学院（人工智能学院）硕士生，主要研究方向为异构图神经网络和深度学习等。" ]
  [ "邓琨（1980- ），男，博士，嘉兴大学浙江省全省多模态感知与智能系统重点实验室、嘉兴大学信息科学与工程学院副教授、硕士生导师，主要研究方向为网络结构分析、数据挖掘、异构网络分析等。" ]
  [ "刘星妍（1980- ），女，嘉兴大学信息科学与工程学院高级工程师，主要研究方向为数据挖掘、网络结构分析等。" ]
- 基金信息：
  
  教育部人文社会科学研究专项任务项目(22JDSZ3023);教育部产学合作协同育人项目(220603372015422;220604029012441)
- DOI：10.11959/j.issn.1000-0801.2024078
  中图分类号： TP391
- 收稿日期：2024-01-08，
  
  修回日期：2024-03-15，
  
  纸质出版日期：2024-03-20
- 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

WEI Y C , FU X C , SUN Q Y , et al . Heterogeneous graph neural network for privacy-preserving recommendation [C ] // Proceedings of the 2022 IEEE International Conference on Data Mining (ICDM) . Piscataway : IEEE Press , 2022 : 528 - 537 .

杨帅 , 王瑞琴 , 马辉 . 基于多通道的边学习图卷积网络 [J ] . 电信科学 , 2022 , 38 ( 9 ): 95 - 104 .

YANG S , WANG R Q , MA H . Multi-channel based edge-learning graph convolutional network [J ] . Telecommunications Science , 2022 , 38 ( 9 ): 95 - 104 .

WANG J C , ZHANG Y , WANG L X , et al . Multitask hypergraph convolutional networks: a heterogeneous traffic prediction framework [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 10 ): 18557 - 18567 .

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 ( 8 ): 1830 - 1839 .

LIU Q , TAN H S , ZHANG Y M , et al . Dynamic heterogeneous network representation method based on meta-path [J ] . Acta Electronica Sinica , 2022 , 50 ( 8 ): 1830 - 1839 .

YU P Y , FU C F , YU Y W , et al . Multiplex heterogeneous graph convolutional network [C ] // Proceedings of the Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . New York : ACM , 2022 : 2377 – 2387 .

陶涛 , 李珍 , 王冀彬 , 等 . 基于图神经网络的权益推荐技术方案研究 [J ] . 电信科学 , 2023 , 39 ( 8 ): 91 - 101 .

TAO T , LI Z , WANG J B , et al . Research on the graphical convolution neural network based benefits recommendation system strategy [J ] . Telecommunications Science , 2023 , 39 ( 8 ): 91 - 101 .

KIPF T N , WELLING M . Semi-supervised classification with graph convolutional networks [J ] . arXiv preprint , arXiv: 1609.02907 , 2016 .

VELIKOVI P , CUCURULL G , CASANOVA A , et al . Graph attention networks [J ] . arXiv preprint , arXiv: 1710.10903 , 2017 .

WANG X , JI H Y , SHI C , et al . Heterogeneous graph attention network [C ] // Proceedings of the WWW '19: The World Wide Web Conference . New York : ACM , 2019 : 2022 - 2032 .

FU X Y , ZHANG J N , MENG Z Q , et al . MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding [C ] // Proceedings of the Proceedings of The Web Conference 2020 . New York : ACM , 2020 : 2331 - 2341 .

YANG X C , YAN M Y , PAN S R , et al . Simple and efficient heterogeneous graph neural network [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2023 , 37 ( 9 ): 10816 - 10824 .

ZHAO T Y , YANG C , LI Y B , et al . Space4HGNN: a novel, modularized and reproducible platform to evaluate heterogeneous graph neural network [C ] // Proceedings of the Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2022 : 2776 - 2789 .

任嘉睿 , 张海燕 , 朱梦涵 , 等 . 基于元图卷积的异质网络嵌入学习算法 [J ] . 计算机研究与发展 , 2022 , 59 ( 8 ): 1683 - 1693 .

REN J R , ZHANG H Y , ZHU M H , et al . Embedding learning algorithm for heterogeneous network based on meta-graph convolution [J ] . Journal of Computer Research and Development , 2022 , 59 ( 8 ): 1683 - 1693 .

JIN D , HUO C Y , LIANG C D , et al . Heterogeneous graph neural network via attribute completion [C ] // Proceedings of the Proceedings of the Web Conference 2021 . New York : ACM , 2021 : 391 - 400 .

XU W T , XIA Y C , LIU W Q , et al . Shgnn: structure-aware heterogeneous graph neural network [J ] . arXiv preprint , arXiv: 2112.06244 , 2021 .

ZHAN L , JIA T . CoarSAS2hvec: heterogeneous information network embedding with balanced network sampling [J ] . Entropy , 2022 , 24 ( 2 ): 276 .

YUN S , JEONG M , KIM R , et al . Graph transformer networks [J ] . Advances in neural information processing systems , 2019 , 32 .

YANG Y M , GUAN Z Y , LI J X , et al . Interpretable and efficient heterogeneous graph convolutional network [J ] . IEEE Transactions on Knowledge and Data Engineering , 2023 , 35 ( 2 ): 1637 - 1650 .

CHEN K J , LU H , LIU Z , et al . Heterogeneous graph convolutional network with local influence [J ] . Knowledge-Based Systems , 2022 ( 236 ): 107699 .

LYU Q S , DING M , LIU Q , et al . Are we really making much progress? : Revisiting, benchmarking and refining heterogeneous graph neural networks [C ] // Proceedings of the Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . New York : ACM , 2021 : 1150 - 1160 .

DU C G , YAO K C , ZHU H S , et al . Seq-HGNN: learning sequential node representation on heterogeneous graph [J ] . arXiv preprint , arXiv: 2305.10771 , 2023 .

王悦天 , 傅司超 , 彭勤牧 , 等 . 半监督场景下多视角信息交互的图卷积神经网络 [J ] . 软件学报 , 2023 : 1 - 18 .

WANG Y T , FU S C , PENG Q M , et al . Multi-view interaction graph convolutional network for semi-supervised classification [J ] . Journal of Software , 2023 : 1 - 18 .

WANG X , BO D Y , SHI C , et al . A survey on heterogeneous graph embedding: methods, techniques, applications and sources [J ] . IEEE Transactions on Big Data , 2023 , 9 ( 2 ): 415 - 436 .

GUAN W L , JIAO F K , SONG X M , et al . Personalized fashion compatibility modeling via metapath-guided heterogeneous graph learning [C ] // Proceedings of the Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2022 : 482 - 491 .

WU Z Y , LIANG Q Y , ZHAN Z H . Course recommendation based on enhancement of meta-path embedding in heterogeneous graph [J ] . Applied Sciences , 2023 , 13 ( 4 ): 2404 .

CHEN H , HONG P F , HAN W , et al . Dialogue relation extraction with document-level heterogeneous graph attention networks [J ] . Cognitive Computation , 2023 , 15 ( 2 ): 793 - 802 .

DONG Y X , CHAWLA N V , SWAMI A . metapath2vec: scalable representation learning for heterogeneous networks [C ] // Proceedings of the Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York : ACM , 2017 : 135 - 144 .

YAN Y Y , LI C , YU Y W , et al . OSGNN: original graph and subgraph aggregated graph neural network [J ] . Expert Systems with Applications , 2023 ( 225 ): 120115 .

VAN DER M L , HINTON G . Visualizing data using t-SNE [J ] . Journal of Machine Learning Research , 2008 ( 9 ): 2579 - 2605 .

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