A novel framework for trust network analysis: Connectivity-based intuitionistic fuzzy rough digraph

Abstract

Network connectivity analysis enables information source tracing and spread regulation in social systems. While existing studies have explored intuitionistic fuzzy rough (IFR) digraphs to address the representation needs of pervasive uncertainties and dual-polarity information in real-world networks, their neglect of connectivity characteristics has limited applicability in information diffusion scenarios. This study breaks through conventional framework and proposes a connectivity-based IFR digraph model, which achieves comprehensive representation of information oppositionality, uncertainty, and propagative characteristic. First, we explore minimum equivalent intuitionistic fuzzy subgraph (MEIFS) and semi-maximum equivalent intuitionistic fuzzy supergraph (SEIFS). MEIFS preserves original strength of connectedness through minimal arc sets, while SEIFS achieves the same objective via redundant arc augmentation. This complementarity provides a mathematical tool for approximating complex networks. Then, a connectivity-based IFR digraph model is established through the synergy of MEIFS and SEIFS. Finally, according to the co-occurrence characteristics of trust and distrust in society, the community detection algorithm and multi-core-node mining method for IFR trust networks are developed. Comparative analysis with three existing methods demonstrates the superiority of the proposed technique in approximate modeling of adversarial information propagation systems.