A unified method of detecting core-periphery structure and community structure in networks

Area of Science:

• Network Science
• Graph Theory
• Data Mining

Background:

• Complex networks exhibit meso-scale structures like core-periphery and community structures.
• Existing research primarily focuses on community detection, neglecting core-periphery structure analysis.
• Previous methods investigated core-periphery and community detection separately.

Purpose of the Study:

• To develop a unified framework for simultaneous detection of core-periphery and community structures in complex networks.
• To address limitations in current methods by enabling detection of multiple core-periphery pairs and overlapping communities.
• To provide a flexible approach capable of identifying core-periphery structures at different scales.

Main Methods:

• A novel unified framework is proposed for integrated structure detection.
• The algorithm allows for the identification of single and multiple core-periphery structures.
• It facilitates the detection of overlapping nodes within different communities and variable-scale structures.

Main Results:

• The framework successfully detects both core-periphery and community structures simultaneously.
• The method demonstrates the ability to identify multiple core-periphery pairs and overlapping community memberships.
• Performance validation on synthetic and real-world complex networks confirms the method's efficacy.

Conclusions:

• A foundational framework for detecting core-periphery and community structures in complex networks has been established.
• The unified approach offers enhanced capabilities over separate detection methods.
• This work provides a significant advancement in understanding meso-scale network organization.