融合不完整多视图的异质信息网络嵌入方法  

Heterogeneous Information Network Embedding with Incomplete Multi-view Fusion

作　　者：郑苏苏 关东海 袁伟伟[1,2] ZHENG Su-su;GUAN Dong-hai;YUAN Wei-wei(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 211106,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京211106 [2]软件新技术与产业化协同创新中心,南京211106

出　　处：《计算机科学》2021年第9期68-76,共9页Computer Science

基　　金：江苏省重点研发计划项目(BE2019012);国家自然科学基金委员会-中国民用航空局民航联合研究基金项目(U2033202)

摘　　要：异质信息网络(Heterogeneous Information Network,HIN)嵌入将复杂的异质信息映射到低维稠密的向量空间,有利于网络数据的计算和存储。现有的基于多视图的HIN嵌入方法考虑了节点之间的多种语义关系,但忽略了视图的不完整性。大多数视图存在数据缺失,直接融合多个不完整的视图会导致嵌入效果不佳。为此,文中提出了一种融合不完整多视图的HIN嵌入方法(Incomplete Multi-view Fusion Based HIN Embedding,IMHE)。IMHE的关键思想是聚合其他视图的邻居以重建不完整的视图。由于不同的单视图描述的是同一个网络,因此其他视图中的邻居可以一定程度上恢复不完整视图的结构信息。IMHE首先在不同视图中生成节点序列,并利用多头注意力方法学习单视图嵌入。对于每个不完整视图,IMHE在其他视图中找到缺失节点的k阶邻居,然后将不完整视图中邻居的单视图嵌入聚合在一起,为缺失节点生成新的嵌入。最后使用多视图典型相关性分析方法获得节点的统一嵌入,同时提取多个视图的隐藏语义关系。在3个真实数据集上的实验结果表明,相比现有研究,该方法的嵌入性能有显著提升。Heterogeneous information network(HIN)embedding maps complex heterogeneous information to a low-dimensional dense vector space,which is conducive to the calculation and storage of network data.Most existing multi-view-based HIN embedding methods consider multiple semantic relationships between nodes,but ignore the incompleteness of the view.Most of views are incomplete and directly fusing multiple incomplete views will affect the performances of the embedding model.To address this problem,we propose a novel HIN embedding model with incomplete multi-view fusion,named IMHE.The key idea of IMHE is to aggregate neighbors of other views to reconstruct the incomplete views.Since different views describe the same HIN,neighbors in other views can restore the structure information of the missing nodes.The IMHE model first generates nodes sequences in different views,and leverages the multi-head self-attention method to obtain single-view embedding.For each incomplete view,IMHE finds the k-order neighbors of the missing nodes in other views,then aggregates the embeddings of neighbors in the incomplete view to generate new embeddings for missing nodes.IMHE finally uses the multi-view canonical correlation analysis method to obtain the joint embedding of nodes,thereby simultaneously extracting the hidden semantic relationship of multiple views.Experiment results on three real-world datasets show that the proposed method is superior to the state-of-the-art methods.

关 键 词：不完整视图 多视图融合 异质信息网络 网络嵌入 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]