Extreme Value Statistics of Community Detection in Complex Networks with Reduced Network Extremal Ensemble Learning...

Area of Science:

• Network Science
• Machine Learning
• Computational Complexity

Background:

• Identifying network structure, specifically community detection, is a fundamental challenge.
• Optimizing community partitions is often an NP-complete problem, requiring efficient algorithms.
• Extremal Ensemble Learning (RenEEL) offers a novel machine learning approach to improve network partitioning.

Purpose of the Study:

• To investigate the effectiveness of the RenEEL algorithm for community detection in complex networks.
• To empirically study the impact of ensemble size (K) and candidate partition number (L) on RenEEL's performance.
• To relate the performance of RenEEL to extreme value statistics and record-breaking phenomena.

Main Methods:

• RenEEL iteratively refines an ensemble of K partitions.
• Worst partitions are replaced by new partitions derived from analyzing collapsed super-nodes.
• Base partitioning algorithms generate initial and candidate partitions.

Main Results:

• Increasing the ensemble size (K) generally yields better partitions than increasing the number of candidate partitions (L).
• The study establishes a relationship between RenEEL's effectiveness and extreme value statistics.
• Consensus within the ensemble indicates convergence towards an optimal partition.

Conclusions:

• RenEEL is a promising machine learning technique for complex network analysis and community detection.
• Parameter tuning, specifically prioritizing K over L, is crucial for optimizing RenEEL's performance.
• The findings contribute to understanding the efficacy of ensemble-based learning in network science problems.