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

• 作 者: WANG Peng XU BaoWen WU YuRong ZHOU XiaoYu
• 机 构: School of Computer Science and Engineering,Southeast University,State Key Laboratory of Software Eng
• 出 处: Science China(Information Sciences)
• 发表日期: 2015年01期
• 关键词: links topological supervised similarity missing dissolution probabilistic Procee

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 presented. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are presented. Finally, some future challenges of the link prediction in social networks are discussed.