基于密集连接卷积神经网络的链路预测模型  

Link prediction model based on densely connected convolutional network

作　　者：王文涛[1] 吴淋涛 黄烨 朱容波[1] WANG Wentao;WU Lintao;HUANG Ye;ZHU Rongbo(College of Computer Science,South-Central University for Nationalities,Wuhan Hubei 430074,China)

机构地区：[1]中南民族大学计算机科学学院

出　　处：《计算机应用》2019年第6期1632-1638,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61772562);中南民族大学中央高校基本科研业务费专项基金资助项目(CZY18014);中南民族大学研究生学术创新基金后期资助项目(3212018hqzz029)~~

摘　　要：现有的基于网络表示学习的链路预测算法主要通过捕获网络节点的邻域拓扑信息构造特征向量来进行链路预测,该类算法通常只注重从网络节点的单一邻域拓扑结构中学习信息,而对多个网络节点在链路结构上的相似性方面研究不足。针对此问题,提出一种基于密集连接卷积神经网络(DenseNet)的链路预测模型(DenseNet-LP)。首先,利用基于网络表示学习算法node2vec生成节点表示向量,并利用该表示向量将网络节点的结构信息映射为三维特征数据;然后,利用密集连接卷积神经网络来捕捉链路结构的特征,并建立二分类模型实现链路预测。在四个公开的数据集上的实验结果表明,相较于网络表示学习算法,所提模型链路预测结果的ROC曲线下方面积(AUC)值最大提高了18个百分点。The current link prediction algorithms based on network representation learning mainly construct feature vectors by capturing the neighborhood topology information of network nodes for link prediction. However, those algorithms usually only focus on learning information from the single neighborhood topology of network nodes, while ignore the researches on similarity between multiple nodes in link structure. Aiming at these problems, a new Link Prediction model based on Densely connected convolutional Network(DenseNet-LP) was proposed. Firstly, the node representation vectors were generated by the network representation learning algorithm called node2 vec, and the structure information of the network nodes was mapped into three dimensional feature information by these vectors. Then, DenseNet was used to to capture the features of link structure and establish a two-category classification model to realize link prediction. The experimental results on four public datasets show that, the Area Under the Receiver Operating Characteristic(ROC) Curve(AUC) value of the prediction result of the proposed model is increased by up to 18 percentage points compared to the result of network representation learning algorithm.

关 键 词：链路预测 网络表示学习 节点表示 卷积神经网络 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]