Hunt, F.
and Pozo, R.
(2020),
Fast Methods for Finding Multiple Effective Influencers in Real Networks, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/jres.125.036
(Accessed December 14, 2024)
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Fast Methods for Finding Multiple Effective Influencers in Real Networks
Published
Author(s)
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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.Citation
Journal of Research (NIST JRES) -
Volume
125
NIST Pub Series
Pub Type
NIST Pubs
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Keywords
approximation algorithms, hitting time, informaton dynamics, Monte Carlo methods, networks, random walks.
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Created December 30, 2020, Updated March 1, 2021