An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

Qibo Sun,Ao Zhou,Guoyu Yang

doi:10.1155/2020/4529429

Abstract

Identifying important nodes in complex networks is essential in disease transmission control, network attack protection, and valuable information detection. Many evaluation indicators, such as degree centrality, betweenness centrality, and closeness centrality, have been proposed to identify important nodes. Some researchers assign different weight to different indicator and combine them together to obtain the final evaluation results. However, the weight is usually subjectively assigned based on the researcher’s experience, which may lead to inaccurate results. In this paper, we propose an entropy-based self-adaptive node importance evaluation method to evaluate node importance objectively. Firstly, based on complex network theory, we select four indicators to reflect different characteristics of the network structure. Secondly, we calculate the weights of different indicators based on information entropy theory. Finally, based on aforesaid steps, the node importance is obtained by weighted average method. The experimental results show that our method performs better than the existing methods.

Highlights

Complex network is playing an important role in our daily life
First we prove the effectiveness of our method by experimenting on the Beijing University of Posts and Telecommunications (BUPT) campus network. en we illustrate the experimental results on Shanxi Water Network and Shanxi Railway Network to see how the proposed method works in more complicated cases. e experiment is conducted on a PC with Intel Core i5-3470 3.2 GHz CPU, 4 GB RAM
Shanxi Railway Network is a part of the transportation network in Shanxi. It provides great convenience for people’s outgoing and commodities trading. e topological structure of Shanxi Water Network is shown in Figure 10. e experimental result is shown in Figure 11. e network is coming to break down after the top 60 nodes have been attacked. e Number of Connected Components (NCC) of Shanxi Water Network obtains the largest ascent with our Complexity method. erefore, EBSAM performs better than other methods

Summary

Introduction

Complex network is playing an important role in our daily life. E Internet connects all the world, so nowadays information spreads faster and wider than before. Electricity companies build their own networks to provide electricity for production and living. Policemen cooperate through their inner networks in catching criminals. Identifying important nodes in complex networks is a critical issue in various situations. We can ease traffic congestion by taking corresponding measures to split traffic flow in certain important nodes. A typical example is mobile edge computing network, where smaller edge clouds connects with each other, but at the same time follow arrangements from larger edge clouds. While mobile users may connect to and accept service from both of them [1]

Methods

Results

Conclusion