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

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 vectors,the embedded structure feature vectors are merged with standardized node attribute feature vectors through early fusion.In the second stage,a deep learning model is constructed to capture the link patterns between node feature vectors through supervised learning,thereby achieving relationship prediction.We select real network data from three different domains,including open-source software development,patent research and development,and scientific collaboration,to examine the effectiveness of the model.Additionally,in the experimental sample networks,we compare the predictive performance of the proposed model with traditional methods such as common neighbors(CN)and resource allocation(RA),deep learning methods that only consider node structural information like deep walk and node2vec,as well as models that can incorporate node attributes like variational graph auto encoders(VGAE)and graph convolutional networks(GCN).The results show that the DDLP model,based on node feature embedding,effectively captures the distribution patterns of links in the network.Its performance(precision,recall,and F 1 score)significantly surpasses that of traditional models based on vector similarity(such as CN and RA)and deep learning models such as node2vec and VGAE.Furthermore,compared to predictive methods that only incorporate network structural information,the integration of node attributes has significantly enhanced the predictive capabilities of both the DDLP model and comparative models such as VGAE and GCN.Particularly,the DDLP model has the highest performance metrics,indicating that the incorporation of node attributes allows it to learn a richer set of rules for link formation,thereby offering superior performance.This also further reveals that it is not enough to predict the link formation only by the similarity of node vectors,and there is a need for more refined processing to enable the model to better learn the rules of link formation within networks.This study not only proposes a deep learning framework that integrates network structure and node attribute information for link prediction but also lays the methodological foundation for related applications,such as system recommendations.In future work,we will explore the portability of the framework to other network analysis tasks such as mechanism analysis of the network formation,link prediction in heterogeneous networks,etc.through more extensive experiments.In addition,we intend to optimize the DDLP model to reduce computational complexity,making it more suitable for link prediction in ultra-large-scale networks.