General optimization technique for high-quality community detection in complex networks.

Area of Science:

• Network Science
• Computational Social Science
• Data Mining

Background:

• Community detection algorithms are crucial for understanding complex networks.
• Existing methods often rely on optimizing objective functions, with modularity being the most prevalent.
• There is a need for more effective and accurate optimization strategies.

Purpose of the Study:

• To present a general and effective search strategy for optimizing various objective functions in community detection.
• To evaluate the performance of this strategy, particularly for modularity optimization.
• To demonstrate the impact of optimization accuracy on network partitioning quality.

Main Methods:

• Development of a general search strategy for community detection.
• Application of the strategy to optimize modularity on real-world and synthetic networks.
• Comparison with existing state-of-the-art algorithms in terms of objective function scores and execution time.

Main Results:

• The proposed search strategy significantly outperforms existing algorithms in achieving higher objective function scores for modularity.
• Execution time is competitive, surpassing most alternatives except for faster but less efficient greedy algorithms.
• The approach can analyze networks up to 30,000 nodes within hours on standard workstations.

Conclusions:

• The developed search strategy offers a highly effective method for accurate community detection in complex networks.
• It provides a good balance between computational efficiency and the quality of network partitioning.
• The findings highlight the critical importance of optimization accuracy for reliable community detection.