Abstract:
We have developed a microscopic version of general concealed voter model (GCVM). Original GCVM uses only statistical-physical methods, while our new approach starts with a real network. A microscopic model is suitable for any two-layer network (with internal and external layers) satisfying the definition given in the paper. We conduct a series of simulations with different network structures and found that a cyclic external structure prolongs consensus time in comparison with a complete external structure. Moreover, a cyclic external structure has a positive impact on a winning rate, and this result is different from the one obtained in the macroscopic version of GCVM. The possible reasons for this difference are discussed in the paper. Additionally, we propose and validate the hypothesis that there exists a strong linear relationship between a consensus time and pairwise average shortest paths $d$ in the network structure. We performed a controlled variable approach to validate the impact of each individual parameter on key performance indicators (KPIs) including a consensus time and winning rate. Furthermore, we assess the influence of parameter combinations on KPIs by analyzing the results using the $K$-means algorithm. We conclude that certain parameter combinations can have a significant impact on the consensus time.
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