Heterogeneous hypernetwork representation learning method with hyperedge constraint

doi:10.11772/j.issn.1001-9081.2022121908

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (12): 3654-3661.DOI: 10.11772/j.issn.1001-9081.2022121908

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

Heterogeneous hypernetwork representation learning method with hyperedge constraint

Keke WANG, Yu ZHU(), Xiaoying WANG, Jianqiang HUANG, Tengfei CAO

Department of Computer Technology and Applications，Qinghai University，Xining Qinghai 810000，China

Received:2022-12-30 Revised:2023-03-23 Accepted:2023-03-28 Online:2023-04-07 Published:2023-12-10
Contact: Yu ZHU
About author:WANG Keke， born in 1999， M. S. candidate. Her research interest is network representation learning.
WANG Xiaoying， born in 1982， Ph. D.， professor. Her research interests include high-performance computing， green computing.
HUANG Jianqiang， born in 1985， Ph. D.， professor. His research interests include high-performance computing， performance analysis.
CAO Tengfei， born in 1987， Ph. D.， associate professor. His research interests include edge computing， privacy protection.
Supported by:
National Natural Science Foundation of China(62166032);Natural Science Foundation of Qinghai Province(2022-ZJ-961Q)

超边约束的异质超网络表示学习方法

王可可, 朱宇(), 王晓英, 黄建强, 曹腾飞

青海大学计算机技术与应用系，西宁 810000

通讯作者: 朱宇
作者简介:王可可（1999—），女，河南濮阳人，硕士研究生，主要研究方向：网络表示学习
王晓英（1982—），女，吉林双辽人，教授，博士，CCF高级会员，主要研究方向：高性能计算、绿色计算
黄建强（1985—），男，陕西西安人，教授，博士，CCF高级会员，主要研究方向：高性能计算、性能分析
曹腾飞（1987—），男，湖北钟祥人，副教授，博士，CCF高级会员，主要研究方向：边缘计算、隐私保护。
基金资助:
国家自然科学基金资助项目(62166032);青海省自然科学基金资助项目(2022?ZJ?961Q)

Abstract

Abstract:

Compared with ordinary networks， hypernetworks have complex tuple relationships， namely hyperedges. However， most existing network representation learning methods cannot capture the tuple relationships. To solve the above problem， a Heterogeneous hypernetwork Representation learning method with Hyperedge Constraint （HRHC） was proposed. Firstly， a method combining clique extension and star extension was introduced to transform the heterogeneous hypernetwork into the heterogeneous network. Then， the meta-path walk method that was aware of semantic relevance among the nodes was introduced to capture the semantic relationships among the heterogeneous nodes. Finally， the tuple relationships among the nodes were captured by means of the hyperedge constraint to obtain high-quality node representation vectors. Experimental results on three real-world datasets show that， for the link prediction task， the proposed method obtaines good results on drug， GPS and MovieLens datasets. For the hypernetwork reconstruction task， when the hyperedge reconstruction ratio is more than 0.6， the ACCuracy （ACC） of the proposed method is better than the suboptimal method Hyper2vec（biased 2nd order random walks in Hyper-networks）， and the average ACC of the proposed method outperforms the suboptimal method， that is heterogeneous hypernetwork representation learning method with hyperedge constraint based on incidence graph （HRHC-incidence graph） by 15.6 percentage points on GPS dataset.

Key words: network representation, hypernetwork, hyperedge constraint, link prediction, hypernetwork reconstruction

摘要：

与普通网络相比，超网络具有复杂的元组关系（超边），然而现有的大多数网络表示学习方法并不能捕获元组关系。针对上述问题，提出一种超边约束的异质超网络表示学习方法（HRHC）。首先，引入一种结合团扩展和星型扩展的方法，从而将异质超网络转换为异质网络；其次，引入感知节点语义相关性的元路径游走方法捕获异质节点之间的语义关系；最后，通过超边约束机制捕获节点之间的元组关系，从而获得高质量的节点表示向量。在3个真实世界的超网络数据集上的实验结果表明，对于链接预测任务，所提方法在drug、GPS和MovieLens数据集上都取得了较好的结果；对于超网络重建任务，当超边重建比率大于0.6时，所提方法在drug数据集上的准确性（ACC）优于次优的Hyper2vec（biased 2nd order random walks in Hyper-networks），同时所提方法在GPS数据集上的ACC超过其他基线方法中次优的基于关联图的超边超边约束的异质超网络表示学习方法（HRHC-关联图）15.6个百分点。

关键词: 网络表示, 超网络, 超边约束, 链接预测, 超网络重建

CLC Number:

TP181

Keke WANG, Yu ZHU, Xiaoying WANG, Jianqiang HUANG, Tengfei CAO. Heterogeneous hypernetwork representation learning method with hyperedge constraint[J]. Journal of Computer Applications, 2023, 43(12): 3654-3661.

王可可, 朱宇, 王晓英, 黄建强, 曹腾飞. 超边约束的异质超网络表示学习方法[J]. 《计算机应用》唯一官方网站, 2023, 43(12): 3654-3661.

Figures/Tables 11

Fig.1 Heterogeneous hypernetwork

Fig.2 Two-section graph

Fig.3 Incidence graph

Fig.4 Two-section graph+incidence graph

Fig.5 TransE model

Fig.6 Framework of HRHC

Tab. 1 Dataset details

数据集	节点类型（节点数）			边数
drug	用户（4）	药物（132）	反应（221）	1 195
GPS	用户（5）	位置（70）	活动（146）	1 436
MovieLens	用户（457）	电影（1 688）	标签（1 530）	5 965

Tab. 2 Binary operator

操作	符号	定义
Weighted-L1	$· 1 ¯$	$f (u) · f (v) 1 ¯ i = f i (u) - f i (v)$
Weighted-L2	$· 2 ¯$	$f (u) · f (v) 2 ¯ i = f i (u) - f i (v) 2$

Tab. 2 Binary operator

操作	符号	定义
Weighted-L1	$· 1 ¯$	$f (u) · f (v) 1 ¯ i = f i (u) - f i (v)$
Weighted-L2	$· 2 ¯$	$f (u) · f (v) 2 ¯ i = f i (u) - f i (v) 2$

Tab. 3 AUC comparison of link prediction

二元算子操作	方法	drug数据集			GPS数据集			MovieLens数据集
二元算子操作	方法	2-截图	关联图	2-截图+ 关联图	2-截图	关联图	2-截图+ 关联图	2-截图	关联图	2-截图+ 关联图
Weighted-L1	DeepWalk	79.12	88.32	88.91	85.40	87.21	90.58	69.20	89.06	90.54
	node2vec	78.54	88.18	88.61	82.99	88.14	90.92	71.36	88.76	90.33
	metapath2vec	84.35	89.26	92.52	89.18	89.69	90.37	80.32	84.25	90.48
	CoarSAS2hvec	86.02	88.14	91.53	88.14	91.92	94.85	93.60	94.77	96.34
	HRTC	88.35	91.58	93.22	90.81	92.67	93.36	92.51	94.05	96.31
	HRHC	87.92	90.46	93.01	87.46	92.67	95.70	92.78	93.14	96.52
	Hyper2vec		92.37			92.78			96.18
	HPSG		91.97			93.20			98.08
	HPHG		94.86			94.33			91.64
	Event2vec		95.28			96.05			93.45
Weighted-L2	DeepWalk	77.08	89.05	87.45	85.22	88.69	90.06	68.77	88.86	90.63
	node2vec	77.96	88.47	88.48	83.85	86.43	91.12	70.37	89.27	91.26
	metapath2vec	84.57	89.44	91.03	87.46	89.18	90.55	78.52	85.22	89.23
	CoarSAS2hvec	87.08	88.77	92.80	87.80	92.61	94.27	92.07	94.14	95.31
	HRTC	87.08	91.84	94.27	90.47	92.53	93.39	91.18	93.47	95.54
	HRHC	88.98	90.04	94.86	87.80	93.12	94.33	92.12	92.47	96.11
	Hyper2vec		93.43			91.92			95.82
	HPSG		92.14			93.12			97.80
	HPHG		95.83			93.47			91.38
	Event2vec		95.43			94.33			91.91

Fig. 7 Hypernetwork reconstruction

Fig. 8 Parameter sensitivity analysis

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Heterogeneous hypernetwork representation learning method with hyperedge constraint

超边约束的异质超网络表示学习方法

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