利用网络结构熵研究复杂网络的演化规律

Complex Networks Evolution Research Using the Network Structure Entropy

利用网络结构熵作为网络演化的指标来研究不同网络的演化规律。选取了3种典型的网络包括无尺度网络、随机网络和规则网络,以及构建的与实际比较接近的网络模型,另外,为了避免由于网络结构熵的定义不准确,还选择3种不同定义的网络结构熵,从而研究网络结构熵在网络演化过程中的变化趋势,最后得出不同类型的网络在网络演化过程中,网络结构熵的差别较大,同时可以利用网络结构熵的增长率作为网络演化的指标,网络演化初期,网络结构熵增长率较大,随着网络规模逐渐增大,网络结构基本稳定,网络结构熵的增长率也逐渐降低。

Different types of network evolution have different characteristics. To study the charac- teristics of the different network evolution, we try to take advantage of the network structure en- tropy as network evolution indicators. We also choose the three typical network including the scale-free network, the random network, the star network and construct a network model similar to the real social network. In addition, in order to avoid the inaccurate of the network structure entropy definition, we also choose three different definitions of the network structure entropy to study the network evolution trend. Finally we get the conclusion that the network structure en- tropy of different types of networks in the evolution process is different. We can also use the growth rate of the network structure entropy as the indicator of the network evolution. The growth rate is big in the early network evolution. As the network size increases, the network structure is more stable and the growth rate decreases.