Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization  

作　　者：Minghu Tang Wei Yu Xiaoming Li Xue Chen Wenjun Wang Zhen Liu 

机构地区：[1]key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission,Qinghai Minzu University,Xining,810007,China [2]School of Computer Science,Qinghai Minzu University,Xining,810007,China [3]College of Intelligence and Computing,Tianjin University,Tianjin,300350,China [4]School of International Business,Zhejiang Yuexiu University,Shaoxing,312069,China [5]Law School,Tianjin University,Tianjin,300072,China [6]Graduate School of Engineering,Nagasaki Institute of Applied Science,Nagasaki,851-0193,Japan

出　　处：《Computer Systems Science & Engineering》2022年第12期1069-1084,共16页计算机系统科学与工程（英文）

基　　金：supported by the Teaching Reform Research Project of Qinghai Minzu University,China(2021-JYYB-009);the“Chunhui Plan”Cooperative Scientific Research Project of the Ministry of Education of China(2018).

摘　　要：Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model

关 键 词：Link prediction COLD-START nonnegative matrix factorization graph regularization 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]