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Resampling-based gap analysis for detecting nodes with high centrality on large social network
Citation
Ohara, K and Saito, K and Kimura, M and Motoda, H, Resampling-based gap analysis for detecting nodes with high centrality on large social network, Proceedings of the Advances in Knowledge Discovery and Data Mining 19th Pacific-Asia Conference (PAKDD 2015), 19-22 May 2015, Ho Chi Minh City, Vietnam, pp. 135-147. ISBN 978-3-319-18037-3 (2015) [Refereed Conference Paper]
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Copyright Statement
Copyright 2015 Springer International Publishing
Official URL: http://doi.org.10.1007/978-3-319-18038-0 11
DOI: doi:10.1007/978-3-319-18038-0_11
Abstract
We address a problem of identifying nodes having a high
centrality value in a large social network based on its approximation
derived only from nodes sampled from the network. More specifically,
we detect gaps between nodes with a given confidence level, assuming
that we can say a gap exists between two adjacent nodes ordered in
descending order of approximations of true centrality values if it can
divide the ordered list of nodes into two groups so that any node in one
group has a higher centrality value than any one in another group with
a given confidence level. To this end, we incorporate confidence intervals
of true centrality values, and apply the resampling-based framework to
estimate the intervals as accurately as possible. Furthermore, we devise
an algorithm that can efficiently detect gaps by making only two passes
through the nodes, and empirically show, using three real world social
networks, that the proposed method can successfully detect more gaps,
compared to the one adopting a standard error estimation framework,
using the same node coverage ratio, and that the resulting gaps enable
us to correctly identify a set of nodes having a high centrality value.
Item Details
Item Type: | Refereed Conference Paper |
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Keywords: | gap analysis, error estimation, resampling, node centrality |
Research Division: | Information and Computing Sciences |
Research Group: | Distributed computing and systems software |
Research Field: | Networking and communications |
Objective Division: | Expanding Knowledge |
Objective Group: | Expanding knowledge |
Objective Field: | Expanding knowledge in the information and computing sciences |
UTAS Author: | Motoda, H (Dr Hiroshi Motoda) |
ID Code: | 109270 |
Year Published: | 2015 |
Web of Science® Times Cited: | 1 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2016-06-06 |
Last Modified: | 2018-01-17 |
Downloads: | 0 |
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