移动社交信任评估——以新浪微博为例
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摘要
文章基于新浪微博的真实移动社交互动数据,考虑了体现关系互动的社交关系强度和社交影响范围,以及体现信息互动的信息价值和信息传播控制力等四个影响因素来构建信任评估模型。利用熵权法计算得到各因素对用户信任度的贡献程度。最后,通过计算节点的用户信任度来统一量化用户信任,从而识别可信节点。
Based on the real mobile social interactive data of Sina Weibo,the social trust evaluation model is constructed by taking the intensity of social relations and the sphere of social influence,which embody relationship interaction,as well as the information value and information transmission control,which embody information interaction into account.Then,entropy weight method is used to calculate the contribution of each factor to users' trust degree.Finally,users' social trust is quantified uniformly by calculating the node users' trust degree in order to identify the trusted nodes.
Based on the real mobile social interactive data of Sina Weibo,the social trust evaluation model is constructed by taking the intensity of social relations and the sphere of social influence,which embody relationship interaction,as well as the information value and information transmission control,which embody information interaction into account.Then,entropy weight method is used to calculate the contribution of each factor to users' trust degree.Finally,users' social trust is quantified uniformly by calculating the node users' trust degree in order to identify the trusted nodes.
引文
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[2] AL-MUTAIRI A,YAHYAOUI H,RAAFAT H.Lattice-based ranking for service trust behaviors[J].Knowledge-Based Systems,2016,102(C):20-38.
[3] CURTY R,ZHANG P.Social commerce:looking back and forward[J].Proceedings of the American Society for Information Science and Technology,2011,48(1):1-10.
[4] EIRINAKI M,LOUTA M D,VARLAMIS I.A trust-aware system for personalized user recommendations in social networks[J].IEEE Transactions on Systems,Man,and Cybernetics Systems,2014,44(4):409-421.
[5] LIU G,ZHU F,ZHENG K,LIU A,LI Z,ZHAO L.TOSI:a trust-oriented social influence evaluation method in contextual social networks[J].Neurocomputing,2016,210:130-140.
[6] 鲍宇,曾国荪,曾连荪,陈波,王伟.P2P网络中防止欺骗行为的一种信任度计算方法[J].通信学报,2008,29(10):215-222.
[7] eBay Site[EB/OL].http://www.ebay.com.
[8] Amazon Site[EB/OL].http://www.amazon.com.
[9] 周国强,曾庆凯.一种角色分离的信任评估模型[J].软件学报,2012,23(12):3187-3197.
[10] 张继东,李鹏程.移动社交网络用户信任度量化模型构建研究[J].情报理论与实践,2017,40(5):56-60.
[11] AU YEUNG C,IWATA T.Strength of social influence in trust networks in product review sites[C].WSDM,2011:495-504.
[12] JEONG S,NOH G,OH H.,KIM C K.Follow spam detection based on cascaded social information[J].Information Sciences,2016,369(C):481-499.
[13] MEO P D,MESSINA F,ROSACI D,SARNE G,VASILAKOSAV.Estimating graph robustness through the randic index[J].IEEE Transactions on Cybernetics,2017,99:1-14.
[14] MEO P D,MESSINA F,ROSACI D,SARNé G M L.Recommending users in social networks by integrating local and global reputation[M].Switzerland:Springer International Publishing,2014.
[15] 陈婷,朱青,周梦溪,王珊.社交网络环境下基于信任的推荐算法[J].软件学报,2017,28(3):721-731.
[16] 张继东,段小萌.基于移动社交平台的用户信任度分析研究[J].现代情报,2017(9):93-96,102.
[17] GRANOVETTER M.The strength of weak ties:a network theory revisited[J].Sociological Theory,1983,1(6):201-233.
[18] CHEN D B,GAO H,Lü L,ZHOU T.Identifying influential nodes in large-scale directed networks:the role of clustering[J].PLoS One,2013,8(10):e77455.
[19] NEWMAN M E.Spread of epidemic disease on networks[J].Physical Review E Statistical Nonlinear & Soft Matter Physics,2002,66(1):016128.
[20] AL-GARADI M A,VARATHAN K D,RAVANA S D.Identification of influential spreaders in online social networks using interaction weighted k-core decomposition method[J].Physica A:Statistical Mechanics and its Applications,2016,468:278-288.
[21] DOLEV S,ELOVICI Y,PUZIS R.Routing betweenness centrality[J].Journal of the ACM,2010,57(4):1-27.
[22] 程启月.评测影响因素权重确定的结构熵权法[J].系统工程理论与实践,2010,30(7):1225-1228.
[23] 王莉,程学旗.在线社会网络的动态社区发现及演化[J].计算机学报,2015,38(2):219-237.