Random Samplings Using Metropolis Hastings Algorithm

  • Miguel Arcos-ArgudoEmail author
  • Rodolfo Bojorque-Chasi
  • Andrea Plaza-Cordero
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 965)


Random Walks Samplings are important method to analyze any kind of network; it allows knowing the network’s state any time, independently of the node from which the random walk starts. In this work, we have implemented a random walk of this type on a Markov Chain Network through Metropolis-Hastings Random Walks algorithm. This algorithm is an efficient method of sampling because it ensures that all nodes can be sampled with a uniform probability. We have determinate the required number of rounds of a random walk to ensuring the steady state of the network system. We concluded that, to determinate the correct number of rounds with which the system will find the steady state it is necessary start the random walk from different nodes, selected analytically, especially looking for nodes that may have random walks critics.


Markov chains Small worlds Metropolis hastings Random walks Node sampling Random sampling 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Miguel Arcos-Argudo
    • 1
    Email author
  • Rodolfo Bojorque-Chasi
    • 1
  • Andrea Plaza-Cordero
    • 1
  1. 1.Research Group on Artificial Intelligence and Assistance Technologies (GIIATA)Salesian Polytechnic UniversityCuencaEcuador

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