Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks
Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks
复制标题
基于深度置信网络的签名社交网络中链接预测方法
DOI：
10.3390/e17042140
复制
发表时间：
2015-04-01
期刊：
影响因子：
2.7
通讯作者：
Wang, Xiaolong
中科院分区：
文献类型：
--
作者：
Liu, Feng;Liu, Bingquan;Wang, Xiaolong
在一些在线社交网络服务（SNS）中，允许成员标记他们与其他人的关系，并且这种关系可以表示为具有带符号值（正或负）的链接。包含这种关系的网络称为符号社交网络（SSN），一些现实世界的复杂系统也可以用SSN建模。给定SSN的观测结构的信息，链路预测的目的是估计未观测链路的值。注意到，大多数以前的链接预测方法是基于成员的相似性和监督学习方法，然而，研究工作的调查隐藏的原则，驱动社会成员的行为很少进行。本文提出了基于深度信念网络（DBN）的链接预测方法。包括无监督链接预测模型、特征表示方法和基于DBN的链接预测方法。在三个不同领域的SNS（social networking services）数据集上进行了实验，结果表明，该方法能够高效地预测链接值，并具有良好的泛化能力。
In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative). The networks containing such relations are named signed social networks (SSNs), and some real-world complex systems can be also modeled with SSNs. Given the information of the observed structure of an SSN, the link prediction aims to estimate the values of the unobserved links. Noticing that most of the previous approaches for link prediction are based on the members’ similarity and the supervised learning method, however, research work on the investigation of the hidden principles that drive the behaviors of social members are rarely conducted. In this paper, the deep belief network (DBN)-based approaches for link prediction are proposed. Including an unsupervised link prediction model, a feature representation method and a DBN-based link prediction method are introduced. The experiments are done on the datasets from three SNSs (social networking services) in different domains, and the results show that our methods can predict the values of the links with high performance and have a good generalization ability across these datasets.