A simulated annealing heuristic for maximum correlation core/periphery partitioning of binary networks.

Area of Science:

• Network analysis
• Computational social science
• Graph theory

Background:

• Core-periphery structures are crucial for understanding social and organizational networks.
• Existing methods for identifying core-periphery structures often rely on computationally intensive heuristic algorithms.
• Maximizing correlation between ideal and observed network structures is a key objective.

Purpose of the Study:

• To develop a computationally efficient simulated annealing algorithm for maximum correlation core/periphery partitioning.
• To apply the algorithm to binary networks, enabling clear actor categorization.
• To evaluate the algorithm's performance on simulated and real-world networks.

Main Methods:

• A novel simulated annealing algorithm was developed for binary network partitioning.
• The algorithm's efficiency and accuracy were tested on simulated networks up to 2000 actors.
• The method was applied to analyze problem-solving, trust, and information-sharing networks within a company.

Main Results:

• The proposed simulated annealing algorithm provides a computationally efficient solution for core-periphery partitioning.
• The algorithm effectively identifies core and periphery groups based on network structure correlations.
• Empirical analysis demonstrated the model's utility in real-world organizational network contexts.

Conclusions:

• The developed algorithm offers a significant advancement in analyzing network structures.
• Efficient core-periphery partitioning can enhance the understanding of organizational dynamics and information flow.
• This method provides a valuable tool for researchers and practitioners in network science.