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Information trust, inference and transfer in social and information networks.

机译:社会和信息网络中的信息信任,推断和传递。

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摘要

In this thesis, our overarching goal is to aggregate crowdsourced information that is collected from computing systems based on social networks and represented in information networks. Due to the autonomous nature of such a social computing paradigm, the crowdsourced information is often subject to low quality, contributed by susceptible information sources without a reliant quality control scheme. Thus, to reveal the trustworthiness of the involved information sources, we aim to explore the social dependency behind the social networks where information contributors are prone to be influenced by each other. We explored the impact of such social dependency between sources on the information trust, aggregation and quality in social computing models. On the other hand, we will also investigate the structure underlying information shared by sources to reveal their trustworthiness. Our study will deepen our understanding of the patterns and behaviors of information sources and their reliability from both social and information aspects. Several closely related problems are investigated in this thesis: (1) the source trustworthiness, which aims to distinguish the untrustworthy sources from the trustworthy ones; (2) social signal processing, which aims to aggregate the multi-source contributed information to recover the true signals behind the problems such as the correct answers to a question and the true labels for an image; (3) the social dependency, which reveals the mutual influences among different sources; and (4) the nature of information structure, such as the information dependency underlying low-rank structure and visual similarities. Our goal is to propose a unified probabilistic model to explain the social and information phenomena behind these problems. In this thesis, we designed several algorithms which are tested in several real social and information network scenarios. Superior performances have been achieved compared with many existing state-of-the-art technologies in the areas.
机译:在本文中,我们的总体目标是汇总众包信息,这些信息是从基于社交网络的计算系统中收集并在信息网络中表示的。由于这种社交计算范式的自治性,众包信息通常质量低下,这是由易受攻击的信息源在没有可靠的质量控制方案的情况下造成的。因此,为了揭示相关信息源的可信赖性,我们旨在探索社交网络背后的社会依赖性,在这些社交网络中,信息提供者容易受到彼此的影响。我们探讨了源之间的这种社会依赖性对社会计算模型中的信息信任,汇总和质量的影响。另一方面,我们还将研究源共享信息的基础结构,以揭示其可信度。我们的研究将加深我们对信息源的模式和行为及其从社会和信息方面的可靠性的理解。本文研究了与之密切相关的几个问题:(1)源可信度,旨在区分不可信源和可信源。 (2)社交信号处理,旨在汇总多源贡献信息以恢复问题背后的真实信号,例如问题的正确答案和图像的真实标签; (3)社会依赖性,揭示了不同来源之间的相互影响; (4)信息结构的性质,例如低等级结构和视觉相似性所基于的信息依赖性。我们的目标是提出一个统一的概率模型来解释这些问题背后的社会和信息现象。在本文中,我们设计了几种算法,并在几种实际的社交和信息网络场景中进行了测试。与该地区许多现有的最先进技术相比,已经实现了卓越的性能。

著录项

  • 作者

    Qi, GuoJun.;

  • 作者单位

    University of Illinois at Urbana-Champaign.;

  • 授予单位 University of Illinois at Urbana-Champaign.;
  • 学科 Electrical engineering.;Computer science.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 98 p.
  • 总页数 98
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:41:25

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