Motif-based functional backbone extraction of complex networks

Jie Cao, Cuiling Ding and Benyun Shi

Physica A: Statistical Mechanics and its Applications, 2019, vol. 526, issue C

Abstract: As a natural abstraction of a large number of real-world systems, the structure and function of complex networks have been attracting increasing attentions in recent years. Existing studies have highlighted the statistical heterogeneity of connection patterns in large-scale networks, where valuable information is usually overwhelmed by redundant intricacy. In this case, the extraction of truly relevant nodes/connections of a large-scale network, namely, network backbones, can help form reduced but meaningful representations of a large-scale complex network and understand its fundamental structure and function. However, so far as we know, most existing backbone extraction methods focus mainly on the extraction of structural backbones, such as centrality-based backbones. Few studies have studied the problem of how to extract the functional backbones of a network, which is relevant to certain functional properties of the network. Accordingly, in this paper, we present two motif-based extraction methods to extract functional backbones of complex networks based on higher-order organization of salient motifs. One is built upon the global threshold method, and the other is based on the disparity filter method. We implement our proposed methods on a set of real-world networks to evaluate the performance. The results show that our extraction methods are more effective than other existing methods in terms of extracting functional backbones of a network, measured by motif centrality, motif degree, and motif abundance.

Keywords: Functional backbone; Network motif; Motif centrality; Disparity filter; Motif abundance (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037843711930679X
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:526:y:2019:i:c:s037843711930679x

DOI: 10.1016/j.physa.2019.121123

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().