A Network Model Approach for the Degree Correlation Mixing Pattern

Many social, biological or technological systems are recognized as complex networks with assortative or disassortative mixing pattern, which is lacking in typical theorical network models, such as, Watts-Strogatz small-world model or Barabási-Albert scale-free network. In this paper, we propose a mechanism which models the emergence of the degree correlation property of complex networks. Numerical simulations indicate that this correlation-adjustable network model can exhibit fundamentally different degree correlation mixing patterns with a scale-free degree sequence. Moreover, the structural properties of networks, such as the average path length and clustering property are also investigated.