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。
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.
作者
王可可
朱宇
王晓英
黄建强
曹腾飞
WANG Keke;ZHU Yu;WANG Xiaoying;HUANG Jianqiang;CAO Tengfei(Department of Computer Technology and Applications,Qinghai University,Xining Qinghai 810000,China)
出处
《计算机应用》
CSCD
北大核心
2023年第12期3654-3661,共8页
journal of Computer Applications
基金
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)。
关键词
网络表示
超网络
超边约束
链接预测
超网络重建
network representation
hypernetwork
hyperedge constraint
link prediction
hypernetwork reconstruction
