Quantifying complex network information based on communicability sequence entropy

Viewing the network system from the perspective of information, it is a key issue to construct a measure containsthe overall information of the network in the network information theory. The communicability sequence entropy basedon node pair communication capability becomes considered a candidate measure to quantify network information.In order to explore its ability to characterize the global information of the network, we first study the influencesof the topology of the synthetic networks on the communicability sequence entropy. The results show that networks with strong heterogeneity,strong degree-degree correlation and community structure have relatively smaller communicability sequence entropy.Moreover, by studying the communicability sequence entropy of some real networks and their correspondingrandomized network models, we find that the higher the order of the randomized network modelis, the smaller communicability sequence entropy is, and the closer it is to the real networkscommunicability sequence entropy. These results show that the communicability sequence entropyof the network is sensitive to the basic topology of the network, and tends to decrease with increasing the order degreeof the network. The conclusions provide evidence for the ability of the communicability sequence entropy to quantify the overall information of the network.