TWIT:社交网络中局部信任值的双向计算  

TWIT:two-way algorithm for local trust inferring in social networks

作　　者：李凤岐[1] 李光明[1] 杨南海[1] 俞闯 夏锋[1] 

机构地区：[1]大连理工大学国家示范性软件学院,辽宁大连116621

出　　处：《计算机工程与应用》2016年第4期66-73,共8页Computer Engineering and Applications

摘　　要：在线社交网络是移动互联网时代最成功的服务之一,好友的可信任程度成为用户首要关注的问题,针对如何避免个人信息被恶意用户窃取和泄露,提出了一种双向的计算局部信任值的算法TWIT(Two-Way algorithm to Infer Trust),从可信任用户与恶意用户两个方向综合推断用户间的信任值,并且考虑到朋友关系的可传递性,进一步结合机器学习中的分类算法提出了改进算法MTWIT,消除了必须存在网络路径这一前提限制,并且在信任值的推断正确率方面取得了一定的优势,同时确保了算法能够适应社交网络拓扑结构动态变化的特点。在新浪微博数据集上的实验结果充分说明了这一结论。Online social network is one of the most successful services in mobile Internet. How much one can trust others has been one of users＇ most concerned questions. To avoid private information being exposed by malicious users,a two-way algorithm called TWIT is proposed,to calculate local trust values. It does the inference from the view of both trustable and untrustable users. Taking the transferability of the friends relationships into account,MTWIT algorithm is proposed based on the idea of machine learning,to eliminate the restriction of the precondition that there must exists a path between any two users,and take the advantage of inferring trust values with higher accuracy in the meantime. Moreover,it can adapt to dynamic topology in social network. The experimental results on datasets from Sina microblog fully illustrate this conclusion.

关 键 词：社交网络 双向推理算法(TWIT) 信任值推断 机器学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] G203[自动化与计算机技术—控制科学与工程]