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We propose a strategy for achieving maximum cooperation in evolutionary gameson complex networks. Each individual is assigned a weight that is proportionalto the power of its degree, where the exponent alpha is an adjustable parameterthat controls the level of diversity among individuals in the network. Duringthe evolution, every individual chooses one of its neighbors as a referencewith a probability proportional to the weight of the neighbor, and updates itsstrategy depending on their payoff difference. It is found that there exists anoptimal value of alpha, for which the level of cooperation reaches maximum.This phenomenon indicates that, although high-degree individuals play aprominent role in maintaining the cooperation, too strong influences from thehubs may counterintuitively inhibit the diffusion of cooperation. We provide aphysical theory, aided by numerical computations, to explain the emergence ofthe optimal cooperation. Other pertinent quantities such as the payoff, thecooperator density as a function of the degree, and the payoff distribution,are also investigated. Our results suggest that, in order to achieve strongcooperation on a complex network, individuals should learn more frequently fromneighbors with higher degrees, but only to certain extent.

Opinion Dynamic with agents immigration