Open Access
2015 Extracting Backbones from Weighted Complex Networks with Incomplete Information
Liqiang Qian, Zhan Bu, Mei Lu, Jie Cao, Zhiang Wu
Abstr. Appl. Anal. 2015(SI07): 1-11 (2015). DOI: 10.1155/2015/105385


The backbone is the natural abstraction of a complex network, which can help people understand a networked system in a more simplified form. Traditional backbone extraction methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency—the exhaustive search of all nodes or edges is often prohibitively expensive. In this paper, we propose a backbone extraction heuristic with incomplete information (BEHwII) to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way, which only relies on the local/incomplete knowledge rather than the global view of the network. Experimental results on four real-life networks demonstrate the advantage of BEHwII over the classic disparity filter method by either effectiveness or efficiency validity.


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Liqiang Qian. Zhan Bu. Mei Lu. Jie Cao. Zhiang Wu. "Extracting Backbones from Weighted Complex Networks with Incomplete Information." Abstr. Appl. Anal. 2015 (SI07) 1 - 11, 2015.


Published: 2015
First available in Project Euclid: 15 April 2015

zbMATH: 1367.68268
MathSciNet: MR3326635
Digital Object Identifier: 10.1155/2015/105385

Rights: Copyright © 2015 Hindawi

Vol.2015 • No. SI07 • 2015
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