Information trust, inference and transfer in social and information networks

Qi, Guo-Jun

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https://hdl.handle.net/2142/46854 Copy

Description

Title

Information trust, inference and transfer in social and information networks

Author(s)

Qi, Guo-Jun

Issue Date

2014-01-16T18:18:46Z

Director of Research (if dissertation) or Advisor (if thesis)

Huang, Thomas S.

Doctoral Committee Chair(s)

Huang, Thomas S.

Committee Member(s)

• Aggarwal, Charu C.
• Han, Jiawei
• Hasegawa-Johnson, Mark A.
• Liang, Zhi-Pei

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• information trust
• information inference
• information transfer
• information networks
• social networks

Abstract

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.

Graduation Semester

2013-12

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http://hdl.handle.net/2142/46854

Copyright and License Information

Copyright 2013 GuoJun Qi

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