Mining Multiplex Structural Patterns from Complex Networks

  • Bo Yang
  • Jiming LiuEmail author
Part of the Web Information Systems Engineering and Internet Technologies Book Series book series (WISE)


Wisdom Web of Things (W2T) can be modeled and studied from the perspective of complex networks. The complex network perspective aims to model and characterize complex systems that consist of multiple and interdependent components. Among the studies on complex networks, topological structure analysis is of the most fundamental importance, as it represents a natural route to understand the dynamics, as well as to synthesize or optimize the functions, of networks. A broad spectrum of network structural patterns have been respectively reported in the past decade, such as communities, multipartites, hubs, authorities, outliers, bow ties, and others. In this chapter, we show that many real-world networks demonstrate multiplex structure patterns. A multitude of known or even unknown (hidden) patterns can simultaneously exist in the same network, and moreover they may be overlapped and nested with each other to collaboratively form a heterogeneous, nested or hierarchical organization, in which different connective phenomena can be observed at different granular levels. In addition, we show that such patterns hidden in exploratory networks can be well defined as well as effectively recognized within an unified framework consisting of a set of proposed concepts, models, and algorithms. Our findings provide a strong evidence that many real-world complex systems are driven by a combination of heterogeneous mechanisms that may collaboratively shape their ubiquitous multiplex structures as we currently observe. This work also contributes a mathematical tool for analyzing different sources of networks from a new perspective of unveiling multiplex structure patterns, which will be beneficial to Wisdom Web of Things.


Cash Flow Structural Pattern Hierarchical Organization Isomorphism Subgraph Synthetic Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This chapter is based on the authors’ work published in [1], with further extended materials on detailed theoretical analysis as well as additional experimental results.

This work was supported in part by National Natural Science Foundation of China under grants 61373053 and 61572226, Program for New Century Excellent Talents in University under grant NCET-11-0204, Jilin Province Natural Science Foundation under grants 20150101052JC, and Hong Kong Research Grants Council under grant RGC/HKBU211212 and HKBU12202415.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Hong Kong Baptist UniversityKowloon TongHong Kong
  3. 3.International WIC InstituteBeijing University of TechnologyBeijingChina

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