Opinion dynamics on social proximity disordered networks
Understanding how local social pressure shapes collective opinion formation is essential in modern society, with implications in sociology, politics, finance, and technology. Complex networks are a powerful framework for investigating these processes and for representing social interactions in emergent phenomena, and canonical models such as random, scale-free, and small-world networks remain most relevant when Euclidean geometric proximity and contiguous interactions are not the dominant organizing factors. Our investigation explores the effects of Gabriel graphs, a spatially embedded network that utilizes proximity-based interactions, on social opinion dynamics. Using a geometric disorder parameter that randomly displaces agent coordinates, we transition from square lattices of social interactions to spatial random networks that preserve local constraints while introducing strong topological heterogeneity. Opinion dynamics operate through social pressure, with a level of nonconformity that affects dissent among opinions. We analyze magnetization, susceptibility, and Binder's fourth-order cumulant to characterize the emergence and breakdown of global social consensus. The model exhibits second-order phase transitions whose behavior depends on both geometric disorder and nonconformity levels. We use volumetric scaling and provide a reference frame appropriate for characterizing criticality in regular and heterogeneous interaction topologies. We find that the standard critical exponents remain compatible with the two-dimensional Ising universality class. Furthermore, the results suggest that spatially constrained, planar, and globally connected interaction topologies preserve the dimensionality of space in critical dynamics analysis.