作者机构：
[张力; 青涛; 蒋建军; 李鹏程; 王以群] Human Factors Institute, School of Economic &, Management, University of South China, Hengyang, China;[张晓玲; 伍大清] School of Computer Science and Technique, University of South China, Hengyang, China;[李敏] Networks Center, University of South China, Hengyang, China;[彭玉元] School of Computer Engineering, Guangzhou College of South China University of Technology, Guangzhou, China;[张力] Hunan Institute of Technology, Hengyang, China
作者机构：
[刘立; 伍大清] Computer Science and Technology Institute, University of South China, Hengyang, China;[伍大清] Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing, China;[伍大清] Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, China;[郑建国; 朱君璇; 伍大清; 赵燕] School of Business and Management, Donghua University, Shanghai, China;[伍大清] Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Province, Chengdu University, Chengdu, China
作者机构：
[阳小华; 刘朝晖; 刘杰] School of Computer Science and Technology, University of South China, Hengyang, 421001, China;[陈智; 吴志强] Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610041, China
通讯机构：
School of Computer Science and Technology, University of South China, China
作者机构：
[丁琳] School of Computer Science and Technology, University of South China, Hengyang, 421001, China;[张嗣瀛] Institute of Complexity Science, Qingdao University, Qingdao, 266071, China
通讯机构：
[Ding, L.] S;School of Computer Science and Technology, University of South China, Hengyang, China
作者机构：
[郑建国; 伍大清] Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China;[伍大清] Artificial Intelligence Key Laboratory of Sichuan Province, Zigong 643000, China;[伍大清] Department of Computer Science and Technology, University of South China, Hengyang 421001, China
通讯机构：
Glorious Sun School of Business and Management, Donghua University, China
作者机构：
[张嗣瀛; 丁琳] Institute of Complexity Science, Qingdao University, Qingdao 266071, China;[丁琳] School of Computer Science and Technology, University of South China, Hengyang 421001, China
通讯机构：
Institute of Complexity Science, Qingdao University, China
作者机构：
[蒋黎明] School of Computer Science and Technology, University of South China, Hengyang, Hunan 421001, China;[张琨; 蒋黎明; 徐建; 张宏] School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China
通讯机构：
School of Computer Science and Technology, University of South China, China
关键词：
D-S证据理论;信任表达;信任传递;信任聚合;信任子图
摘要：
基于图论方法提出了一种新的证据信任模型(graph theory based evidential trust model,GTETM),解决了现有证据信任模型中普遍存在的在信任聚合过程中缺少对信任链之间依赖关系的有效处理等引起的模型性能下降问题.同时,GTETM在建模实体的信任度时区分实体的服务信任度与反馈信任度,并在证据理论框架下提出两种不同的信任传递方法,增强了模型抵抗恶意推荐攻击的能力.仿真实验表明,与已有信任度量模型相比,GTETM具有更强的抑制策略欺骗及共谋行为的能力,在信任度量准确性方面也有较大提高.
