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Published on: October 19, 2021
Characterizing and extracting multiplex patterns in complex networks
Bo Yang1, Jiming Liu, Dayou Liu
1Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, School of Computer Science and Technology, Jilin University, Changchun 130012, China. ybo@jlu.edu.cn
Summary
This study introduces a novel method for identifying complex multiplex patterns within networks. Using a granular stochastic blockmodel and machine learning, it effectively characterizes and extracts these intricate structures for deeper network analysis.
Area of Science:
- Complex Systems Science
- Network Science
- Data Mining
Background:
- Complex network theory is crucial for understanding interdependent systems.
- Structural analysis of networks reveals dynamics and guides optimization.
- Existing research identifies various network patterns like communities and hubs.
Purpose of the Study:
- To address the challenge of characterizing and extracting multiplex patterns in complex networks.
- To develop a robust methodology for analyzing coexisting structural patterns.
- To offer a new perspective on complex network exploration.
Main Methods:
- Utilizing a granular stochastic blockmodel for pattern characterization.
- Developing algorithms based on machine learning and statistical inference.
- Applying these methods to effectively extract multiplex patterns.
Main Results:
- Demonstrated successful characterization of multiplex patterns.
- Achieved effective extraction of these complex coexisting structures.
- Validated the proposed models and algorithms.
Conclusions:
- Multiplex patterns in complex networks can be effectively identified and extracted.
- The granular stochastic blockmodel provides a powerful framework for this analysis.
- This work opens new avenues for exploring and understanding complex network structures.
