Link prediction in social networks: the state-of-the-art  

作　　者：WANG Peng XU BaoWen WU YuRong ZHOU XiaoYu 

机构地区：[1]School of Computer Science and Engineering,Southeast University [2]State Key Laboratory of Software Engineering,Wuhan University [3]State Key Laboratory for Novel Software Technology,Nanjing University

出　　处：《Science China(Information Sciences)》2015年第1期1-38,共38页中国科学（信息科学）（英文版）

基　　金：supported by National Key Basic Research and Development Program of China (Grant No. 2014CB340702);National Natural Science Foundation of China (Grant Nos. 61170071, 91318301, 61321491, 61472077);China Postdoctoral Science Foundation (Grant No. 2014M560378);Foundation of the State Key Laboratory of Software Engineering (SKLSE)

摘　　要：In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.

关 键 词：social network link prediction dynamic network similarity metric learning model 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]