Robustness analysis of random hyper-networks based on the internal structure of hyper-edges

Author:

Zhou Bin12,Ma Xiujuan1,Ma Fuxiang1,Gao Shujie12

Affiliation:

1. School of Computer, Qinghai Normal University, Xining 810008, China

2. The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining 810008, China

Abstract

<abstract><p>Random hyper-network is an important hyper-network structure. Studying the structure and properties of random hyper-networks, which helps researchers to understand the influence of the hyper-network structure on its properties. Currently, studies related to the influence of the internal structure of the hyper-edge on robustness have not been carried out for research on the robustness of hyper-networks. In this paper, we construct three $ k $-uniform random hyper-networks with different structures inside hyper-edges. The nodes inside hyper-edges are connected in the ways randomly connected, preferentially connected and completely connected. Meanwhile, we propose a capacity-load model that can describe the relationship between the internal structure and the robustness of the hyper-edge, based on the idea of capacity-load model. The robustness of the three hyper-networks was obtained by simulation experiments. The results show the variation of the internal structure of hyper-edge has a large influence on the robustness of the $ k $-uniform random hyper-network. In addition, the larger number of ordinary edges $ m_{k} $ inside the hyper-edges and the size of the hyper-network $ k $, the more robust the $ k $-uniform random hyper-network is.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

Reference28 articles.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Synchronization analyze of k-uniform hyper-networks;Scientific Reports;2024-03-13

2. The Analysis of Phase Synchronisation in the Uniform Scale-Free Hypernetwork;Communications in Computer and Information Science;2023

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