An information propagation model for social networks based on continuous-time quantum walk

Existing social network simulation models exhibit several limitations, including extensive iteration requirements and multiple control parameters. In this study, an information propagation model based on continuous-time quantum walk (CTQW-IPM) is introduced to rank crucial individuals in undirected social networks. In the proposed CTQW-IPM, arbitrary individuals (or groups) can be specified as initial diffusion dynamic elements through preset probability amplitudes. Information diffusion on a global reachable path is then simulated by an evolution operator, as individual degrees of cruciality are estimated from probability distributions acquired from quantum observations. CTQW-IPM does not require iterations, due to the non-randomness of CTQW, and does not include extensive computations as complex cascade diffusion processes are replaced by evolution operators. Experimental comparisons of CTQW-IPM and several conventional models showed their ranking of crucial individuals exhibited a strong correlation, with nearly every individual in the social network assigned a unique measured value based on the rate of distinguishability. CTQW-IPM also outperformed other algorithms in influence maximization problems, as measured by the resulting spread size.