Complexity Measure of Automobile Industry Site Network Based on Motif

In order to provide the methods for measuring the complexity of huge scale networks and put forward the countermeasures for structural layout and optimization of automobile industry site network, the automobile industry site network topology was built based on the linked data of WWW pages. The clustering algorithm of VOSviewer and software Gephi were used for community detection, and the result demonstrates the content classification system, the functional structure and the small-world property of automobile industry site networks. The Rand-ESU algorithm was used for motif detection, and the networks structure entropy algorithm based on motif was proposed to measure the complexity of each communities. The results of empirical analysis show that the network motifs structure is isomorphic among the different communities of automobile industry site network, which have led to the function similarity of information dissemination in different communities, and the motifs size, the diversified channels for information transmission have an important impact on complexity of networks' structure.