Analysis of parallel evolution of multiple complex network models based on search efficiency

Many real complex networks present the scale-free property.However,why do these networks comply with the preferential attachment rule in their growing? The existing studies have not stated a powerful explanation yet.A reasonable hypothesis is that:if a network fails to comply with the rule of preferential attachment,it will be at a disadvantage in its competition with the other networks.In order to verify this hypothesis,we adopt searching efficiency as a criterion to quantitatively evaluate different evolutionary models.First,the paper proposes a new parallel evolution model of complex network,ensuring that different sub-networks in the same network comply with different evolutionary model.Therefore,we can compare the search efficiency of different evolution models on a uniform basis.We construct the heterogeneous complex network based on BA scale-free network,WS small world network and ER random network.Second,random walk search strategy and DS maximum degree search strategy are applied to compare the search efficiency of the different evolutionary models and to explain the homogenization of the evolution model in a complex network.The Information Barrier phenomenon is found,that is the nodes of a disadvantage network model are difficult to be accessed by the nodes of other models.The experimental results show that:scale-free network is the most adaptive model for searching.This conclusion explains the existence of scale-free phenomenon in many real complex networks to some extent.