INITIAL STUDY ON TRUST COMPUTATION BASED ON TENSOR AND LIE ALGEBRA IN COMPLEX NETWORKS

Abstract

Trust computation in complex networks is a crucial topic for systems

involving interactions among agents, such as recommendation platforms,

multi-agent systems, and social networks. This paper presents a novel

framework combining tensor-based feature modeling and Lie algebraic

dynamics to formally define and compute trust. We model measures

such as dispatch, familiarity, responsibility, and influence in tensor form

and propose a trust function based on weighted features. We then use Lie

algebra to encode the structural evolution of trust over time and derive

key mathematical properties. This approach enables robust and dynamic

trust inference in large-scale and evolving networks.