Information spreading on metapopulation networks with heterogeneous contacting

Extensive real-data reveals that individuals exhibit heterogeneous contacting frequency in social systems. We propose a mathematical model to investigate the effects of heterogeneous contacting for information spreading in metapopulation networks. In the proposed model, we assume the number of contacting (NOC) distribution follows a specific distribution, including the normal, exponential, and power-law distributions. We utilize the Markov chain method to study the information spreading dynamics and find that mean and variance display no significant effect on the outbreak threshold for all the considered distributions. Under the same values of NOC distribution's mean and variance, the information prevalence is largest when the distribution of NOC follows the normal distribution and second-largest for the exponential distribution, the smallest for the power-law distribution. When the distribution of NOC obeys the normal distribution, experimental results show that the information prevalence will decrease with individual contact ability heterogeneity. We observe similar phenomena when the distribution of NOC follows a power-law and exponential distribution. Furthermore, a larger mean of individual contact capacity distribution will result in higher information prevalence.

Automated summary

This study investigates the effects of heterogeneous contacting for information spreading in social systems using real data and a mathematical model. The researchers find that the type of distribution for individual contacting frequency does not significantly affect the outbreak threshold, but it does have an impact on the information prevalence. Specifically, information prevalence is highest when the distribution follows a normal distribution, second highest for exponential distribution, and lowest for power-law distribution.