  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.
