Reduced network extremal ensemble learning (RenEEL) scheme for community detection in complex networks
This work addresses the challenge of efficiently finding optimal community partitions in complex networks, representing an incremental improvement over existing ensemble methods.
The authors tackled the problem of community detection in complex networks by introducing the RenEEL scheme, which uses extremal ensemble learning to iteratively update partitions and maximize modularity, and they found that it outperforms all other known methods on benchmark networks.
We introduce an ensemble learning scheme for community detection in complex networks. The scheme uses a Machine Learning algorithmic paradigm we call Extremal Ensemble Learning. It uses iterative extremal updating of an ensemble of network partitions, which can be found by a conventional base algorithm, to find a node partition that maximizes modularity. At each iteration, core groups of nodes that are in the same community in every ensemble partition are identified and used to form a reduced network. Partitions of the reduced network are then found and used to update the ensemble. The smaller size of the reduced network makes the scheme efficient. We use the scheme to analyze the community structure in a set of commonly studied benchmark networks and find that it outperforms all other known methods for finding the partition with maximum modularity.