融合网络结构与节点属性的关系预测深度学习方法研究  

Deep Learning Method Integrating Network Structure andNode Attribute for Link Prediction

作　　者：刘鹏 桂亮 王慧蓉 夏昊翔[2] LIU Peng;GUI Liang;WANG Huirong;XIA Haoxiang(School of Economics and Management,Jiangsu University of Science and Technology,Zhenjiang 212100,China;School of Economics and Management,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]江苏科技大学经济管理学院,江苏镇江212100 [2]大连理工大学经济管理学院,辽宁大连116024

出　　处：《运筹与管理》2024年第7期158-165,共8页Operations Research and Management Science

基　　金：国家自然科学基金资助项目(71871108)。

摘　　要：关系预测是网络科学领域的一个重要研究问题。传统基于相似性的启发式方法难以完成大规模或稀疏网络的关系预测任务,虽然近来兴起的基于深度学习的方法可以解决这一问题,但大多数工作主要通过网络结构信息嵌入表示向量的相似性实现关系预测。许多实证研究表明网络关系的形成会受到节点属性的影响,同时相似性也不是关系形成的唯一准则。本文提出了融合网络结构与节点属性进行关系预测的DDLP模型。该模型借助早期融合的方式获取网络结构信息和节点属性信息的嵌入表示,进而通过节点特征向量与连边信息的有监督学习实现关系预测。现实网络中的实验结果表明,DDLP模型可以有效捕捉网络中的连边规律,特别是融合节点属性后,其预测性能(精确率、召回率和F 1值)明显优于比对模型。本研究不仅为关系预测的相关工作提出了一个深度学习模型框架,也为诸如系统推荐的现实应用奠定方法基础。In reality,social systems from various domains can be effectively characterized through network models,often exhibiting structural properties distinct from random networks,such as small-world and scale-free characteristics.The formation of these non-trivial structural properties is closely associated with the establishment of relationships(i.e.,links)among individuals(i.e.,nodes)in the network.Consequently,accurately predicting potential relationships in the network not only helps deepen our understanding of the underlying mechanisms driving network formation but also further elucidates the relationship between network topology and system function.Thus,the prediction of links between nodes has become an important research problem in the field of network science.For link prediction,a commonly used method is heuristic algorithms based on similarity.However,in more complex network scenarios,such methods struggle to effectively address high-dimensional non-linear problems resulting from network scale expansion or node feature growth.In recent years,the emergence of deep learning-based approaches has provided new opportunities by transforming complex network information into low-dimensional representation vectors.However,most existing deep learning-based approaches primarily achieve link prediction through the similarity of embedding representation vectors of network structures.Many empirical studies indicate that the formation of links in the network is influenced by node attributes,and similarity alone is not the sole criterion for link formation.Therefore,the link prediction approach based on deep learning is worth further exploration.In this paper,we propose a deep walk-deep neural network for link prediction(DDLP)model,which integrates network structure and node attribute information for link prediction.This model consists of two stages,i.e.,the stage of node feature embedding and the stage of link prediction.In the first stage,network structure information is embedded using deep walks.Then,to obtain node feature

关 键 词：关系预测 深度学习 网络结构 节点属性 

分 类 号：N93[自然科学总论] C939[经济管理—管理学]