The Barabási and Albert scale-free network model

Former complex network models focused on preferential attachment to get scale-free feature, lacking random connection mechanism that inevitably exists in real-life network. In this paper, we deeply study one new complex network model, named the Preferential-Random network model, by introducing rand om attachment to the evolving procedure of the BA scale-free model. The analysis and experiment results show that this new model is better in keeping with real networks, which reveals closer ties between neighbors as well as obvious small world feature. Significantly, the new model explains the 2–3 range of power-law exponent in real-life scale-free networks, and it has stronger robustness against intentional attacks compared with the BA model. Focusing on the influence of random attachment on scale-free networks, our research may provide an effective guidance for modeling and constructing more reliable and realistic networks.