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How do complex networks evolve? Is there a mathematically rigorous approach to naturally capture the microscopic behaviors of network dynamics and yet it can still lead to tractable analysis of the marcoscopic behaviors in large-scale complex networks? Aiming to obtain a fundamental understanding of network dynamics, we study network evolution from an entropic and Markovian perspective. Specifically,...
Complex network theory is put forward in following research to analyze the statistical parameters of network business statically in E-commerce area. Empirical Study is based on the data provided by an online shopping website and statistical parameters such as enterprise act degree and clustering coefficient is studied accordingly. Thus we found that E-commerce displays the power law and clustering...
Network value has attracted more and more attentions from scholars and enterprisers. Information through network can essentially impact everyone, while everyone in the network has the structure function to the network and may also influence others. But the contribution to the network is not the same for every one. Based on the complex network theory and customer activity, a customer network value...
A SIRS epidemic model related to feedback mechanism, crowd density and the moving activity of individual on complex networks is proposed in this paper. The theoretical analysis and computer simulation indicate that feedback mechanism can decay the stable infection ratio of system, and however, it can not influence the spreading threshold of system. With descending of crowd density, the stable infection...
Complex networks can be classified in three main types: random networks, small-world networks and scale-free networks. In this work we studied the cycles distribution of these three types of networks and then we proposed one universal Gaussian function that mathematically models the distribution of the three classes. The proposed equation can be used to model any network including social networks.
The discovery of scale free network captured new sights for our understanding on complex system. Considering the growth and preferential attachment, BA model provided a lot of enlightenment for us. However, the two basic assumptions in BA model are still too simple for explaining many real phenomena, there are quite far distance to real networks. In this paper, we try to expand BA model, namely in...
In this paper, an edge driven model is proposed which allows all kinds of manipulations of complex networks that are common in most of the real-world networks. It is based on evolution features other than structural features that are employed in other models. Under this framework, a particular model is generated for reproducing the power-law distribution and the analytical results are also given.
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