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. 2013 Jun 10;8(6):e66020.
doi: 10.1371/journal.pone.0066020. Print 2013.

Revealing the hidden relationship by sparse modules in complex networks with a large-scale analysis

Qing-Ju Jiao  1 , Yan Huang, Wei Liu, Xiao-Fan Wang, Xiao-Shuang Chen, Hong-Bin Shen
Affiliations

Revealing the hidden relationship by sparse modules in complex networks with a large-scale analysis

Qing-Ju Jiao et al. PLoS One. .

Abstract

One of the remarkable features of networks is module that can provide useful insights into not only network organizations but also functional behaviors between their components. Comprehensive efforts have been devoted to investigating cohesive modules in the past decade. However, it is still not clear whether there are important structural characteristics of the nodes that do not belong to any cohesive module. In order to answer this question, we performed a large-scale analysis on 25 complex networks with different types and scales using our recently developed BTS (bintree seeking) algorithm, which is able to detect both cohesive and sparse modules in the network. Our results reveal that the sparse modules composed by the cohesively isolated nodes widely co-exist with the cohesive modules. Detailed analysis shows that both types of modules provide better characterization for the division of a network into functional units than merely cohesive modules, because the sparse modules possibly re-organize the nodes in the so-called cohesive modules, which lack obvious modular significance, into meaningful groups. Compared with cohesive modules, the sizes of sparse ones are generally smaller. Sparse modules are also found to have preferences in social and biological networks than others.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Generated synthetic network of this study.
Figure 2
Figure 2. Distributions of nodes in A00 network mined by BTS method and Newman-fast algorithm.
Figure 3
Figure 3. The relative proportions of nodes in different networks from sparse and cohesive modules detected by BTS method.
Figure 4
Figure 4. The average sizes of sparse and cohesive modules in various networks.
Figure 5
Figure 5. Two possible organizations of sparse modules in the network.
Figure 6
Figure 6. The relationship between a3 and E value.
See this image and copyright information in PMC

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