Open AccessApplication of Transition probability based on random graph and Markov chain for the Monitoring of Complex Network
Author(s) -
Hanqi Mao,
Sihua Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1861/1/012063
Subject(s) - markov chain , random graph , variable order markov model , additive markov chain , markov process , state space , graph , continuous time markov chain , markov renewal process , mathematics , markov model , computer science , discrete mathematics , statistics
In recent years, the method of graph theory has been used in the process of solving many practical problems, especially the transition probability of random graphs. Because it can be used to solve the problem of numerical variables with random phenomena, it is also practical. In the middle, the Markov chain provides a transformation and functional relationship from a set of discrete vectors to the state space, and is often used to describe the transition from one initial state to another end state in various state spaces. The process and the transition process are random. The research in this paper is based on the transition probability of the random graph and the Markov chain to establish a mathematical model, so that complex networks can be monitored, including the ability to predict the long-term steady state of complex networks. The required transition time, the quantitative relationship between the number of monitoring points and the reliability and sensitivity of the network were obtained.