A Label Inference Method Based on Maximal Entropy Random Walk over Graphs

摘要：

　　With the rapid development of Internet,graphs have been widely used to model the complex relationships among various entities in real world.However,the labels on the graphs are always incomplete.The accurate label inference is required for many real applications such as personalized service and product recommendation.In this paper,we propose a novel label inference method based on maximal entropy random walk.The main idea is that a small number of vertices in graphs propagate their labels to other unlabeled vertices in a way of random walk with the maximal entropy guidance.We give the algorithm and analyze the time and space complexities.We confirm the effectiveness of our algorithm through conducting experiments on real datasets.

关键词： Label inference Random walk Maximal entropy

作者: Jing Pan Yajun Yang Qinghua Hu Hong Shi

作者单位: School of Computer Science and Technology,Tianjin University,Tianjin,China;Tianjin Key Laboratory of Cognitive Computing and Application,Tianjin,China

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 506-518

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）