Complex Networks: Statistical Properties, Community Structure, and Evolution

Abstract

We investigate the function for different networks based on complex network theory. In this paper, we choose five data sets from various areas to study. In the study of Chinese network, scale-free effect and hierarchical structure features are found in this complex system. These results indicate that the discovered features of Chinese character structure reflect the combination nature of Chinese characters. In addition, we study the community structure in Chinese character network. We can find that community structure is always considered as one of the most significant features in complex networks, and it plays an important role in the topology and function of the networks. Furthermore, we cut all the nodes in the different networks from low degree to high degree and then obtain many networks with different scale. According to the study, two interesting results have been obtained. First, the relationship between the node number of the maximum communities and the number of communities in the corresponding networks is studied and it is linear. Second, when the number of nodes in the maximum communities is increasing, the increasing tendency of the number of its edges slows down; we predict the complex networks have sparsity. The study effectively explains the characteristic and community structure evolution on different networks.

Artificial Intelligence‐Based Real‐Time Signal Sample and Analysis of Multiperson Dragon Boat Race in Complex Networks

“…e complex network theory in network science provides a theoretical basis for the discussion of the above issues [5,6]. e results also provide effective data support for the multilayer perceptron (MLP) model and make the optimistic value obtained more accurate.…”

Empirical topological investigation of practical supply chains based on complex networks

“…[25] The community testing continues to receive attention. [26] Traditional approaches to community detection include spectral analysis, [27][28][29] modularity based methods, [30] edge clustering, [31,32] clique percolation, [33] and so on. [43][44][45] In this work, we extend the spectral analysis methods to the analysis of networks of industrial Internet.…”

Community-Detection Method of Complex Network Based on Node Influence Analysis

“…For undirected networks, this relationship satisfies transitivity and symmetry. Complex networks often exhibit obvious structural properties, such as small-world properties, scalefree properties and community structure properties [3]. Detecting the community structure of the network is important to help study its organizational functions and uncover the hidden inner connections between nodes [4].…”