Opinion Dynamics On The Complex Networks

Complex network is an important methods to study complex system,The goal of complex network research is to understand how the structure of network influence the physics process on these networks.On the basis of complex network theory,the opinion dynamics on the complex network is studied in this thesis.We study the Sznajd model on the two dimension generalized Small-world net-work (GSWN),which is constructed by adding shortcut rateφand the length distri-bution of the shortcut p(ι)～ι^-α on the regular two dimension lattice.The shortcut connects a pair of node lengthιwith probability of p(ι)～ι^-α,theαdetermines the distribution location of the shortcut and have effect on the network structure,the small-world effect enhanced When the parameterφandαgrowing.Comparing of the consensus time p(μ) and decision time p((?)) according to the Monte Carlo simulation, we find that they are depend on the network structure and the ferromagnetic attractor have nontrivial exit probability.A new quality,individual persistence I_i,is introduced.I_i reflects the persistence of individual when facing the influence of others.We take two different strategies of I_i:(ⅰ) the individual persistence is proportional to its degree,i.e.,I_i =κ_i/∑jκ_j,(ⅱ) the individual persistence is distributed in the network that satisfied p(I) = I^-α.The first strategy connects the node's structure with its function,while no connection taking into consideration in the second strategy.The results show that the system will finally evolving into two fixed points as the usual case and the strategy(ⅰ) can reduce the relaxation time and decision time,while the strategy(ⅱ) gets them increased.In the scale-free networks with the tunable strength(noted by Q) of community structure,using asynchronous updating,it is found that the smaller the commu- nity strength Q,the larger the slope of the exponential relaxation time distribution. Then the effect of the initial up-spin concentration p as a function of the finial all up probability E is investigated by taking different initialization strategies,the random node-chosen initialization strategy has no difference under different community strengths,while the strategies of community node-chosen initialization and hub node-chosen initialization are different in finial probability under different Q,and the latter one is more effective in reaching finial state.Many real-world networks are characterized by adaptive changes in their topology depending on the state of their nodes.The interplay of the two evolutions is then a natural issue to be investigated.Moreover,the evolution of the topology and the dynamical processes can drive each other with complex feedback effects.The topology may indeed have an impact on the evolution of the united states,which in its turn determines how the topology can be modified:the network becomes adaptive. So we explore the continuous Sznajd opinion model on an adaptive network,where the link between agents with far apart opinion will get rewired.Our investigations reveal that the adaptation of the network topology fosters cluster formation by enhancing communication between agents,though the Sznajd rule is most effective to achieve a full synchronization of the agents.When the bound confidence above 0.5, the system will turn into a whole finial state.The interplay between dynamics and topology can have important consequences for the spreading of the opinions.We also simulate these in the different system size and get the same result.A review of recent research on the opinion dynamics and the adaptive networks is introduced.