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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Journal of Research of the National Institute of Standards and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.