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首页> 外文期刊>Information Sciences: An International Journal >kappa-FuzzyTrust: Efficient trust computation for large-scale mobile social networks using a fuzzy implicit social graph
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kappa-FuzzyTrust: Efficient trust computation for large-scale mobile social networks using a fuzzy implicit social graph

机译：kappa-FuzzyTrust：使用模糊隐式社交图进行大规模移动社交网络的高效信任计算

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Large-scale mobile social networks (MSNs) facilitate connections between mobile devices and provide an effective mobile computing environment in which users can access, share, and distribute information. In MSNs, users may belong to more than one community or cluster, and overlapping users may play a special role in complex MSNs. For such MSNs, a key problem is how to evaluate or explain user trustworthiness. In this context, trust inference plays a critical role in establishing trusted social links between mobile users. To infer fuzzy trust relations between users in MSNs with overlapping communities, we propose an efficient trust inference mechanism based on fuzzy communities, which we call kappa-FuzzyTrust. We propose an algorithm for detection of community structure in complex networks under fuzzy degree kappa and construct a fuzzy implicit social graph. We then construct a mobile social context including static attributes (such as user profile and prestige) and dynamic behavioural characteristics(such as user interaction partners, interaction familiarity, communication location and time) based on the fuzzy implicit social graph. We infer the trust value between two mobile users using this mobile social context. We discuss the aggregation and propagation of trust values for overlapping users and indirect connected users. Finally, we evaluate the performance of kappa-FuzzyTrust in simulations. The results show the validity of our fuzzy inference mechanism for behavioural trust relationships in MSNs. They also demonstrate that kappa-FuzzyTrust can infer trust values with high precision. (C) 2014 Elsevier Inc. All rights reserved.
机译：大型移动社交网络（MSN）促进了移动设备之间的连接，并提供了有效的移动计算环境，用户可以在其中访问，共享和分发信息。在MSN中，用户可能属于多个社区或群集，并且重叠的用户可能在复杂的MSN中扮演特殊角色。对于此类MSN，关键问题是如何评估或解释用户的信任度。在这种情况下，信任推断在建立移动用户之间的信任社交链接方面起着至关重要的作用。为了推断具有重叠社区的MSN中用户之间的模糊信任关系，我们提出了一种基于模糊社区的有效信任推断机制，我们将其称为kappa-FuzzyTrust。提出了一种基于模糊度kappa的复杂网络社区结构检测算法，并构造了模糊隐式社会图。然后，我们基于模糊隐式社交图构建一个包含静态属性（例如用户配置文件和声望）和动态行为特征（例如用户交互伙伴，交互熟悉度，通信位置和时间）的移动社交环境。我们使用此移动社交上下文推断两个移动用户之间的信任值。我们讨论了重叠用户和间接连接用户的信任值的聚合和传播。最后，我们评估了仿真中kappa-FuzzyTrust的性能。结果表明，我们的模糊推理机制对于MSN中行为信任关系的有效性。他们还证明了kappa-FuzzyTrust可以高精度地推断信任值。 （C）2014 Elsevier Inc.保留所有权利。

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