An Improved Method for Measuring the Complexity in Complex Networks Based on Structure Entropy

Abstract

Although structure entropy is a useful method to measure the complexity of complex networks, there exist shortcomings, such as the limits of network scales and network types. By combining structure entropy and the absolute density of network, a method is improved to effectively measure the complexity of complex networks. For the improved measure, not only the topology of network is considered, but also the scales of network are considered, and the measurement of network complexity is not affected by the network scales and types. Moreover, the complexity of small-world networks, BA scale free networks, Sierpinski self-similarity networks, Erodös-Rényi (ER) random networks and six real networks (i.e., the 9/11 terrorist network, Celegans network, a USAir network, the USA Political blogs network, a collaboration network in science of networks (NetScience) and yeast protein interaction (YPI) network) are measured by employing the method. The results show that the improved method is effective and feasible to measure the complexity of complex networks.