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Resampling-based framework for estimating node centrality of large social network
conference contribution
posted on 2023-05-23, 09:57 authored by Ohara, K, Saito, K, Kimura, M, Motoda, HWe address a problem of efficiently estimating value of a centrality measure for a node in a large social network only using a partial network generated by sampling nodes from the entire network. To this end, we propose a resampling-based framework to estimate the approximation error defined as the difference between the true and the estimated values of the centrality. We experimentally evaluate the fundamental performance of the proposed framework using the closeness and betweenness centralities on three real world networks, and show that it allows us to estimate the approximation error more tightly and more precisely with the confidence level of 95% even for a small partial network compared with the standard error traditionally used, and that we could potentially identify top nodes and possibly rank them in a given centrality measure with high confidence level only from a small partial network.
History
Publication title
Lecture Notes in Artificial Intelligence 8777: Proceedings of the 17th International Conference on Discovery ScienceVolume
8777Editors
S Dzeroski, P Panov, D Kocev, L TodorovskiPagination
228-239ISBN
978-3-319-11811-6Department/School
School of Information and Communication TechnologyPublisher
Springer International PublishingPlace of publication
SwitzerlandEvent title
17th International Conference on Discovery Science (DS 2014)Event Venue
Bled, SloveniaDate of Event (Start Date)
2014-10-08Date of Event (End Date)
2014-10-10Rights statement
Copyright 2014 SpringerRepository Status
- Restricted