Information trust, inference and transfer in social and information networks

information trust;information inference;information transfer;information networks;social networks

Qi, Guo-Jun

关键词: information trust;    information inference;    information transfer;    information networks;    social networks;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/46854/GuoJun_Qi.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

In this thesis, our overarching goal is to aggregate crowdsourced information that is collected fromcomputing systems based on social networks and represented in information networks.Due to the autonomous nature ofsuch a social computing paradigm, the crowdsourced information is often subject to low quality, contributed by susceptibleinformation 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.