{"title":"Fast Methods for Finding Multiple Effective Influencers in Real Networks.","authors":"Fern Y Hunt, Roldan Pozo","doi":"10.6028/jres.125.036","DOIUrl":null,"url":null,"abstract":"<p><p>We present scalable first hitting time methods for finding a collection of nodes that enables the fastest time for the spread of consensus in a network. That is, given a graph <i>G</i> = (<i>V, E</i>) and a natural number <i>k</i>, these methods find <i>k</i> vertices in <i>G</i> that minimize the sum of hitting times (expected number of steps of random walks) from all remaining vertices. Although computationally challenging for general graphs, we exploited the characteristics of real networks and utilized Monte Carlo methods to construct fast approximation algorithms that yield near-optimal solutions.</p>","PeriodicalId":54766,"journal":{"name":"Journal of Research of the National Institute of Standards and Technology","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11415010/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Research of the National Institute of Standards and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.6028/jres.125.036","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0
Abstract
We present scalable first hitting time methods for finding a collection of nodes that enables the fastest time for the spread of consensus in a network. That is, given a graph G = (V, E) and a natural number k, these methods find k vertices in G that minimize the sum of hitting times (expected number of steps of random walks) from all remaining vertices. Although computationally challenging for general graphs, we exploited the characteristics of real networks and utilized Monte Carlo methods to construct fast approximation algorithms that yield near-optimal solutions.
期刊介绍:
The Journal of Research of the National Institute of Standards and Technology is the flagship publication of the National Institute of Standards and Technology. It has been published under various titles and forms since 1904, with its roots as Scientific Papers issued as the Bulletin of the Bureau of Standards.
In 1928, the Scientific Papers were combined with Technologic Papers, which reported results of investigations of material and methods of testing. This new publication was titled the Bureau of Standards Journal of Research.
The Journal of Research of NIST reports NIST research and development in metrology and related fields of physical science, engineering, applied mathematics, statistics, biotechnology, information technology.