Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80061
Title: A low dimensional approach on network characterization
Authors: Li, BYS
Zhan, C 
Yeung, LF
Ko, KT
Yang, G
Issue Date: 2014
Publisher: Public Library of Science
Source: PLoS one, 2014, v. 9, no. 10, e109383, p. 1-12 How to cite?
Journal: PLoS one 
Abstract: In many applications, one may need to characterize a given network among a large set of base networks, and these networks are large in size and diverse in structure over the search space. In addition, the characterization algorithms are required to have low volatility and with a small circle of uncertainty. For large datasets, these algorithms are computationally intensive and inefficient. However, under the context of network mining, a major concern of some applications is speed. Hence, we are motivated to develop a fast characterization algorithm, which can be used to quickly construct a graph space for analysis purpose. Our approach is to transform a network characterization measure, commonly formulated based on similarity matrices, into simple vector form signatures. We shall show that the N x N similarity matrix can be represented by a dyadic product of two N-dimensional signature vectors; thus the network alignment process, which is usually solved as an assignment problem, can be reduced into a simple alignment problem based on separate signature vectors.
URI: http://hdl.handle.net/10397/80061
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0109383
Rights: © 2014 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The following publication Li, B. Y. S., Zhan, C., Yeung, L. F., Ko, K. T., & Yang, G. (2014). A low dimensional approach on network characterization. PLoS ONE, 9(10), e109383, 1-12 is available at https://dx.doi.org/10.1371/journal.pone.0109383
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