Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Characterizing and extracting multiplex patterns in complex networks

Language

English

Abstract

Complex network theory provides a means for modeling and analyzing complex systems that consist of multiple and interdependent components. Among the studies on complex networks, structural analysis is of fundamental importance as it presents a natural route to understanding the dynamics, as well as to synthesizing or optimizing the functions, of networks. A wide spectrum of structural patterns of networks has been reported in the past decade, such as communities, multipartites, bipartite, hubs, authorities, outliers, and bow ties, among others. In this paper, we are interested in tackling the challenging task of characterizing and extracting multiplex patterns (multiple patterns as mentioned previously coexisting in the same networks in a complicated manner), which so far has not been explicitly and adequately addressed in the literature. Our work shows that such multiplex patterns can be well characterized as well as effectively extracted by means of a granular stochastic blockmodel, together with a set of related algorithms proposed here based on some machine learning and statistical inference ideas. These models and algorithms enable us to further explore complex networks from a novel perspective. © 2006 IEEE.

Keywords

Complex networks, machine learning, multiplex patterns, pattern analysis, statistical inference

Publication Date

2012

Source Publication Title

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volume

42

Issue

2

Start Page

469

End Page

481

Publisher

Institute of Electrical and Electronics Engineers

DOI

10.1109/TSMCB.2011.2167751

Link to Publisher's Edition

http://dx.doi.org/10.1109/TSMCB.2011.2167751

ISSN (print)

10834419

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