LIE OPERATORS FOR TRUST DYNAMICS ON THE UNIT SPHERE IN COMPLEX NETWORKS
Abstract
This paper is to introduce a geometric framework for trust modeling
in complex networks in which trust states are embedded as unit vectors in
a Hilbert space. Trust evolution is formulated as a continuous dynamical
system generated by Lie operators acting on the unit sphere, ensuring
norm preservation and stability of the representation. We show that
network interactions naturally induce skew-adjoint operators, leading to
flows that correspond to rotations on the trust sphere. This geometric
structure guarantees that trust propagation follows geodesic trajecto-
ries, providing both mathematical consistency and interpretability. The
proposed framework establishes a connection between graph-based inter-
actions, Lie algebraic dynamics, and geometric deep learning. It offers a
principled foundation for modeling trust propagation with stability, in-
variance, and continuous-time dynamics in complex networked systems.