Department of Computer Science
Characterizing and extracting multiplex patterns in complex networks
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.
Complex networks, machine learning, multiplex patterns, pattern analysis, statistical inference
Source Publication Title
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Institute of Electrical and Electronics Engineers
Link to Publisher's Edition
Yang, Bo, Jiming Liu, and Dayou Liu. "Characterizing and extracting multiplex patterns in complex networks." IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42.2 (2012): 469-481.