Reputation Detection for Information Diffusion in Social Network Systems. (7th July 2022)

• Record Type: Journal Article
• Authors: Zhou, Yifeng; Yu, Fang
• Other Names: Zhou Yu Academic Editor.
• Is Part Of: Complexity. Volume 2022(2022)
• Publication Date: 2022-07-07
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2022/5317738
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Reputation detection in traditional distributed systems (e.g., electronic commerce systems and peer-to-peer systems) relies heavily on the factor of interaction reputation, which can be derived from direct interactions between agents through bidirectional relationships. However, in the current information diffusion in social network systems (SNS) (e.g., Twitter), the characteristic of the unidirectional relationship between agents and the decay property of diffusion will result in lacking direct interactions; therefore, interaction reputations will be difficult to be obtained by agents in a distributed manner. To solve this problem, a novel distributed reputation detection model following the pattern " from path to individual " ( FPTI ) is proposed, which can provide a new reputation factor as an alternative to interaction reputation in such environments. The main idea is that the positive (or negative) observation of an information diffusion process increases (or decreases) the belief of the corresponding diffusion path, which further increases (or decreases) the reputation of each involved agent. Thus, the reputation of a target agent can be assessed by the superimposition of reputations of multiple paths on which this agent has participated in past information diffusion processes. Furthermore, being aware of agent's limited capacity for reputation detection in SNS, we then propose the enhanced FPTI model ( eFPTI ), which simplifies the detection source to reduce detection costs and achieve the approximate performance as FPTI. Theoretical analyses and experimental evaluations validate the efficiency and effectiveness of our models and also show several properties of the models, for example, the robustness for dynamic environments.