在线社会网络中个性化信任评价基础与应用研究
摘要
随着计算机网络技术的普及和发展,在线社会网络已成为人们结交朋友、日常通信、产品推荐等社交活动最为流行的平台和工具。在大规模动态、开放的在线社会网络应用中,评估用户之间的个性化信任程度,在鼓励和促进用户的良性行为、指导用户选择合适的交互对象、提高用户的体验质量、确保整个系统的安全可靠运行等方面,具有不可忽视的作用。个性化信任评价研究具有重要的学术研究价值和应用前景。
相关领域中的信任模型在面向在线社会网络应用时,存在四个主要问题：1)信任算法通常假设存在一个小规模信任图,而如何基于大规模社会网络生成小规模信任图却鲜有研究；所用信任信息由于主观性、动态性而难于获取或维护。2)信任模型未充分考虑在线社会网络中的用户行为特征及其相互影响。在现实生活中,用户被信任的程度与其影响力是密不可分的。研究用户影响力构成和影响的发生规律,对构建完善的信任模型意义重大。3)信任信息整合面临路径依赖和信任衰减两个挑战：当多条信任路径相互交叉时,如何有效进行信任信息的取舍?如何模拟信任信息随路径传播的衰减?4)信任模型的重要应用场景—基于信任的推荐系统不能灵活处理用户观点形成的时间演变性和反映用户的行为特征。
为解决上述问题,本论文结合社会网络分析和信任评价研究前沿,探寻网络结构、用户行为、和信任机制之间的内在联系、规律和动态特性,围绕在线社会网络个性化信任评价问题,在信任图生成、用户社会影响力评估、信任传播与整合、基于信任的推荐等多个方面提出了创新性理论与方法：
(1)提出了基于小世界网络理论的信任图生成框架SWTrust。为解决信任图生成和信任信息通常主观多变而难于获取或维护的问题,借助复杂网络中的小世界网络理论和弱连接理论,提出一种高效的信任图生成框架,利用相对客观的用户活动域信息来构造信任图。在真实信任网络数据集Epinions上进行了大量实验,验证了SWTrust在保证高覆盖率的同时提高了信任路径搜索的效率,并且生成的信任图能够有效地帮助预测信任。
(2)提出了基于特征的细粒度用户社会影响力评估模型FBI。影响力与信任具有不可忽视的交互关系：影响力大的人通常容易被人信任,受信任的人更有可能去影响其他人。以社会网络结构及特征集为输入,结合用户之间影响的可能性以及每个用户自身的重要性来构造初始影响力；通过识别朋友的影响力贡献来进行影响力更新,最后输出每个用户的影响力及其对其他用户影响的可能性。在三个科研合作网络HEPTH、DBLP和AmetMiner上进行了实验和案例分析。结果表明所提FBI模型能够更好的区分用户影响力(重复率低)、所选出的top-k用户的影响范围大、top-k用户的质量高。
(3)设计了基于广义网络流的信任评价方法GFTrust。利用网络流解决信任路径依赖问题,设计节点漏流函数来模拟信任的衰减；通过合理设置初始流量,节约普通网络流模型的结果正则化问题。从算法效率、模型基本性质和恶意行为鲁棒性等多个方面进行了深入细致的分析。在两个真实的信任网络数据集Epinions和Advogato中做了大量实验。结果表明,网络流的使用提高了信任预测的精度,漏流函数的设置降低了信任预测的误差。
(4)设计了信任推荐系统里基于流体动力学的时间演变评分机制FluidRating。现实生活中,人们观点相互影响并随时间变化。创造性地引入流体动力学模型来刻画观点(评分)形成过程。每个用户被映射为一个容器,信任／影响关系被映射为管子来连接用户容器。用户观点是容器中的液体(温度代表评分,高度代表用户对该评分的坚持度),液体可在容器之间流动,代表影响的发生。采用离散模型对多轮液体流动和混合进行计算。目标节点容器的液体温度被采样搜集,并整合为最终评分。在真实信任推荐系统数据集中的实验结果表明FluidRating具有更高的评分预测精度。
With the rapid development of networking techniques and the prevalence of network applications, online social networks (OSNs) have become the most popular tools and platforms for people's social activities, including making new friends, conducting daily communication or product recommendation, and so on. In the dynamic, open and large scale online social applications, in order to encourage users'good behavior, help users choose proper partners and decide whether to conduct further interactions (e.g., information exchange or product recommendation), improve their service using experience, and guarantee the normal function of the whole system, it is very necessary to estimate the trust levels among users. Personalized trust evaluation is proposed exactly for this aim, which has an important value of both theory and practice.
Existing trust models in other related fields (e.g., P2P network, multi agent system, or semantic network) cannot be directly applied into online social networks, due to the following issues:1) Existing algorithms usually assume that there is an existing small trusted graph, for which little work has been done; moreover, information used for constructing trust is usually subjective and dynamically changing, which make it difficult to collect or maintain.2) Trust models usually overlook the features of user behavior and the mutual influence between users. Actually, the degree that a user is being trusted is closely related with his social influence. Therefore, studying how to differentiate users' social influence is meaningful to the construction of a comprehensive trust model.3) Graph-based trust models cannot well address the two challenges of path dependence and trust decay. That is, when multiple trusted paths are overlapped with each other, how will the model choose trust information on those paths? And how will the trust decay through the propagation process?4) One of the most important application scenarios of trust evaluation is trust-based recommendation system. In those systems, predicting users'ratings is usually being taken as a static process, which is inconsistent with real life.
To address those issues, we study the frontier researches in social network analysis and trust evaluation techniques, and we try to discover the latent rules and dynamic features among trust mechanism, network structure, and user behaviors. In this dissertation, we focus on the trust evaluation models and algorithms in online social networks.
Specifically, the contributions of this dissertation are fourfold:
(1) We propose a small world network theory-based trusted graph generation framework, SWTrust. Existing trust algorithms take the base of a small trusted graph, and the information used to construct trust is usually too complicated to get or maintain, and usually subjective and changeable, which make it vulnerable to vicious nodes. With those problems in mind, we use the small-world network characteristics of online social networks and taking advantage of "weak ties", to divide users'neighbors into three categories based on their active domains: local neighbors, longer contacts, and the longest contacts. When conducting breadth-first search algorithms, we select the next hop neighbors uniformly among the above three categories. In this way, the coverage can be guaranteed and the efficiency can be improved. In addition, comparing to other subjective information, user domain is relatively objective and cannot be changed at will, which makes SWTrust more robust.
(2) We propose a fine-grained feature-based social influence evaluation model, FBI. In real life, trust and influence can impact each other:influential people are likely to be trusted by others, while trusted friends have more chance and strength to influence a person. First, we construct a user's initial social influence by exploring two essential factors, that is, the possibility of impacting others, and the importance of the user himself. Second, we design the social influence adjustment model based on the PageRank algorithm by identifying the influence contributions of friends. For the aim of fine-grained evaluation, based on a feature set which includes the related topics and user profiles, we differentiate the feature strength of users and the tie strength of user relations. We also emphasize the effects of common neighbors in conducting influence between two users. Through experimental analysis, our FBI model shows remarkable performance, which can identify all users'social influences with much less duplication (it is less than7%with our model, while more than80%with other degree-based models), while having a larger influence spread with top-k influential users. A case study validates that our model can identify influential users with higher quality.
(3) We design a generalized flow based trust evaluation algorithm, GFTrust. In online social networks (OSNs), to evaluate trust from one user to another indirectly connected user, the trust information in the trusted paths (i.e., paths built through intermediate trustful users) should be carefully treated. Some paths may overlap with each other, which lead to a unique challenge of path dependence, i.e., how to aggregate the trust values of multiple dependent trusted paths. OSNs bear the characteristic of high clustering, which makes the path dependence phenomenon usually happen. Another challenge is trust decay through propagation, i.e., how to propagate trust along a trusted path, considering the possible decay in each node. We analyze the similarity between trust propagation and network flow, and convert a trust evaluation task (with path dependence and trust decay) into a generalized network flow problem. We propose a modified flow-based trust evaluation scheme GFTrust, in which we address path dependence using network flow, and model trust decay with the leakage associated with each node. Experimental results, with the real social network data sets of Epinions and Advogato, demonstrate that GFTrust can predict trust in OSNs with a high accuracy, and verify its preferable properties.
(4) We also design a Fluid dynamics theory based time-evolving rating prediction scheme, FluidRating. The goal of a trust-based recommendation system is to predict unknown ratings based on the ratings expressed by trusted friends. However, most of the existing work only considers the ratings at the current time slot. In real life, a user receives the influence of different opinions sequentially; accordingly, his opinion evolves over time. We propose a novel rating prediction scheme, FluidRating, which uses fluid dynamics theory to reveal the time-evolving formulation process of human opinions. The recommendation is modeled as fluid with two dimensions:the temperature is taken as the "opinion/rating," and its volume is deemed as the "persistency," representing how much one insists on his opinion. When new opinions come, each user refines his opinion through a round of fluid exchange with his neighbors. Opinions from multiple rounds are aggregated to gain a final prediction; both uniform and non-uniform aggregations are tested. Moreover, three sampling approaches are proposed and examined. The experimental evaluation of a real data set validates the feasibility of the proposed model, and also demonstrates its effectiveness.
相关领域中的信任模型在面向在线社会网络应用时,存在四个主要问题：1)信任算法通常假设存在一个小规模信任图,而如何基于大规模社会网络生成小规模信任图却鲜有研究；所用信任信息由于主观性、动态性而难于获取或维护。2)信任模型未充分考虑在线社会网络中的用户行为特征及其相互影响。在现实生活中,用户被信任的程度与其影响力是密不可分的。研究用户影响力构成和影响的发生规律,对构建完善的信任模型意义重大。3)信任信息整合面临路径依赖和信任衰减两个挑战：当多条信任路径相互交叉时,如何有效进行信任信息的取舍?如何模拟信任信息随路径传播的衰减?4)信任模型的重要应用场景—基于信任的推荐系统不能灵活处理用户观点形成的时间演变性和反映用户的行为特征。
为解决上述问题,本论文结合社会网络分析和信任评价研究前沿,探寻网络结构、用户行为、和信任机制之间的内在联系、规律和动态特性,围绕在线社会网络个性化信任评价问题,在信任图生成、用户社会影响力评估、信任传播与整合、基于信任的推荐等多个方面提出了创新性理论与方法：
(1)提出了基于小世界网络理论的信任图生成框架SWTrust。为解决信任图生成和信任信息通常主观多变而难于获取或维护的问题,借助复杂网络中的小世界网络理论和弱连接理论,提出一种高效的信任图生成框架,利用相对客观的用户活动域信息来构造信任图。在真实信任网络数据集Epinions上进行了大量实验,验证了SWTrust在保证高覆盖率的同时提高了信任路径搜索的效率,并且生成的信任图能够有效地帮助预测信任。
(2)提出了基于特征的细粒度用户社会影响力评估模型FBI。影响力与信任具有不可忽视的交互关系：影响力大的人通常容易被人信任,受信任的人更有可能去影响其他人。以社会网络结构及特征集为输入,结合用户之间影响的可能性以及每个用户自身的重要性来构造初始影响力；通过识别朋友的影响力贡献来进行影响力更新,最后输出每个用户的影响力及其对其他用户影响的可能性。在三个科研合作网络HEPTH、DBLP和AmetMiner上进行了实验和案例分析。结果表明所提FBI模型能够更好的区分用户影响力(重复率低)、所选出的top-k用户的影响范围大、top-k用户的质量高。
(3)设计了基于广义网络流的信任评价方法GFTrust。利用网络流解决信任路径依赖问题,设计节点漏流函数来模拟信任的衰减；通过合理设置初始流量,节约普通网络流模型的结果正则化问题。从算法效率、模型基本性质和恶意行为鲁棒性等多个方面进行了深入细致的分析。在两个真实的信任网络数据集Epinions和Advogato中做了大量实验。结果表明,网络流的使用提高了信任预测的精度,漏流函数的设置降低了信任预测的误差。
(4)设计了信任推荐系统里基于流体动力学的时间演变评分机制FluidRating。现实生活中,人们观点相互影响并随时间变化。创造性地引入流体动力学模型来刻画观点(评分)形成过程。每个用户被映射为一个容器,信任／影响关系被映射为管子来连接用户容器。用户观点是容器中的液体(温度代表评分,高度代表用户对该评分的坚持度),液体可在容器之间流动,代表影响的发生。采用离散模型对多轮液体流动和混合进行计算。目标节点容器的液体温度被采样搜集,并整合为最终评分。在真实信任推荐系统数据集中的实验结果表明FluidRating具有更高的评分预测精度。
With the rapid development of networking techniques and the prevalence of network applications, online social networks (OSNs) have become the most popular tools and platforms for people's social activities, including making new friends, conducting daily communication or product recommendation, and so on. In the dynamic, open and large scale online social applications, in order to encourage users'good behavior, help users choose proper partners and decide whether to conduct further interactions (e.g., information exchange or product recommendation), improve their service using experience, and guarantee the normal function of the whole system, it is very necessary to estimate the trust levels among users. Personalized trust evaluation is proposed exactly for this aim, which has an important value of both theory and practice.
Existing trust models in other related fields (e.g., P2P network, multi agent system, or semantic network) cannot be directly applied into online social networks, due to the following issues:1) Existing algorithms usually assume that there is an existing small trusted graph, for which little work has been done; moreover, information used for constructing trust is usually subjective and dynamically changing, which make it difficult to collect or maintain.2) Trust models usually overlook the features of user behavior and the mutual influence between users. Actually, the degree that a user is being trusted is closely related with his social influence. Therefore, studying how to differentiate users' social influence is meaningful to the construction of a comprehensive trust model.3) Graph-based trust models cannot well address the two challenges of path dependence and trust decay. That is, when multiple trusted paths are overlapped with each other, how will the model choose trust information on those paths? And how will the trust decay through the propagation process?4) One of the most important application scenarios of trust evaluation is trust-based recommendation system. In those systems, predicting users'ratings is usually being taken as a static process, which is inconsistent with real life.
To address those issues, we study the frontier researches in social network analysis and trust evaluation techniques, and we try to discover the latent rules and dynamic features among trust mechanism, network structure, and user behaviors. In this dissertation, we focus on the trust evaluation models and algorithms in online social networks.
Specifically, the contributions of this dissertation are fourfold:
(1) We propose a small world network theory-based trusted graph generation framework, SWTrust. Existing trust algorithms take the base of a small trusted graph, and the information used to construct trust is usually too complicated to get or maintain, and usually subjective and changeable, which make it vulnerable to vicious nodes. With those problems in mind, we use the small-world network characteristics of online social networks and taking advantage of "weak ties", to divide users'neighbors into three categories based on their active domains: local neighbors, longer contacts, and the longest contacts. When conducting breadth-first search algorithms, we select the next hop neighbors uniformly among the above three categories. In this way, the coverage can be guaranteed and the efficiency can be improved. In addition, comparing to other subjective information, user domain is relatively objective and cannot be changed at will, which makes SWTrust more robust.
(2) We propose a fine-grained feature-based social influence evaluation model, FBI. In real life, trust and influence can impact each other:influential people are likely to be trusted by others, while trusted friends have more chance and strength to influence a person. First, we construct a user's initial social influence by exploring two essential factors, that is, the possibility of impacting others, and the importance of the user himself. Second, we design the social influence adjustment model based on the PageRank algorithm by identifying the influence contributions of friends. For the aim of fine-grained evaluation, based on a feature set which includes the related topics and user profiles, we differentiate the feature strength of users and the tie strength of user relations. We also emphasize the effects of common neighbors in conducting influence between two users. Through experimental analysis, our FBI model shows remarkable performance, which can identify all users'social influences with much less duplication (it is less than7%with our model, while more than80%with other degree-based models), while having a larger influence spread with top-k influential users. A case study validates that our model can identify influential users with higher quality.
(3) We design a generalized flow based trust evaluation algorithm, GFTrust. In online social networks (OSNs), to evaluate trust from one user to another indirectly connected user, the trust information in the trusted paths (i.e., paths built through intermediate trustful users) should be carefully treated. Some paths may overlap with each other, which lead to a unique challenge of path dependence, i.e., how to aggregate the trust values of multiple dependent trusted paths. OSNs bear the characteristic of high clustering, which makes the path dependence phenomenon usually happen. Another challenge is trust decay through propagation, i.e., how to propagate trust along a trusted path, considering the possible decay in each node. We analyze the similarity between trust propagation and network flow, and convert a trust evaluation task (with path dependence and trust decay) into a generalized network flow problem. We propose a modified flow-based trust evaluation scheme GFTrust, in which we address path dependence using network flow, and model trust decay with the leakage associated with each node. Experimental results, with the real social network data sets of Epinions and Advogato, demonstrate that GFTrust can predict trust in OSNs with a high accuracy, and verify its preferable properties.
(4) We also design a Fluid dynamics theory based time-evolving rating prediction scheme, FluidRating. The goal of a trust-based recommendation system is to predict unknown ratings based on the ratings expressed by trusted friends. However, most of the existing work only considers the ratings at the current time slot. In real life, a user receives the influence of different opinions sequentially; accordingly, his opinion evolves over time. We propose a novel rating prediction scheme, FluidRating, which uses fluid dynamics theory to reveal the time-evolving formulation process of human opinions. The recommendation is modeled as fluid with two dimensions:the temperature is taken as the "opinion/rating," and its volume is deemed as the "persistency," representing how much one insists on his opinion. When new opinions come, each user refines his opinion through a round of fluid exchange with his neighbors. Opinions from multiple rounds are aggregated to gain a final prediction; both uniform and non-uniform aggregations are tested. Moreover, three sampling approaches are proposed and examined. The experimental evaluation of a real data set validates the feasibility of the proposed model, and also demonstrates its effectiveness.
引文
1 目前已有新的Epinions数据集包含了“类别”信息,详见http://www.public.asu.edu/～jtang20/datasetcode/truststudy.htm。
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