2024/11/01 by Tania Ghosh, Ghosh, Tania, R. K. P. Zia +3
Computer Science · #Anomaly Detection Techniques and Applications #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Face and Expression Recognition #Seismology and Earthquake Studies
paper · pdf · doi:10.48550/arxiv.2411.00977
openalex publication_date 2024/11/01 · openalex created_date 2024/11/15 · openalex updated_date 2026/07/28
Arguably, the most fundamental problem in Network Science is finding structure within a complex network. One approach is to partition the nodes into communities that are more densely connected than one expects in a random network. "The" community structure then corresponds to the partition that maximizes Modularity, an objective function that quantifies this idea. Finding the maximizing partition, however, is a computationally difficult, NP-Complete problem. We explore using a recently introduced machine-learning algorithmic scheme to find the structure of benchmark networks. The scheme, known as RenEEL, creates an ensemble of K partitions and updates the ensemble by replacing its worst member with the best of L partitions found by analyzing a simplified network. The updating continues until consensus is achieved within the ensemble. We perform an empirical study of three real-world networks to explore how the Modularity of the consensus partition depends on the values of K and L and relate the results to the extreme value statistics of record-breaking. We find that increasing K is generally more effective than increasing L for finding the best partition.