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Detection of core-periphery structure in networks based on 3-tuple motifs
Chuang Ma1, Bing-Bing Xiang1, Han-Shuang Chen2
1School of Mathematical Science, Anhui University, Hefei 230601, People's Republic of China.
Chaos (Woodbury, N.Y.)
|June 3, 2018
Summary
This study introduces an effective algorithm for detecting core-periphery (CP) structure in complex networks using a 3-tuple motif. The method accurately identifies various CP structures in both synthetic and real-world systems.
Area of Science:
- Network Science
- Graph Theory
- Data Analysis
Background:
- Mesoscale structure detection is crucial for understanding complex networks.
- Core-periphery (CP) structure is a significant mesoscale organization observed in real-world systems.
Purpose of the Study:
- To propose an effective algorithm for detecting core-periphery (CP) structure.
- To utilize a 3-tuple motif for identifying CP structures.
Main Methods:
- Define a 3-tuple motif based on edge patterns and node properties.
- Construct a motif adjacency matrix.
- Formulate the detection problem as minimizing smallest motif conductance.
Main Results:
- The algorithm effectively detects single or multiple, local or global CP structures.
- Validation on synthetic and empirical networks demonstrates high performance.
Conclusions:
- The proposed 3-tuple motif-based algorithm is an effective tool for core-periphery structure detection.
- The method shows robust performance across diverse network types and CP structure configurations.
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