  We present a novel active learning algorithm for community detection on
networks. Our proposed algorithm uses a Maximal Expected Model Change (MEMC)
criterion for querying network nodes label assignments. MEMC detects nodes that
maximally change the community assignment likelihood model following a query.
Our method is inspired by detection in the benchmark Stochastic Block Model
(SBM), where we provide sample complexity analysis and empirical study with SBM
and real network data for binary as well as for the multi-class settings. The
analysis also covers the most challenging case of sparse degree and
below-detection-threshold SBMs, where we observe a super-linear error
reduction. MEMC is shown to be superior to the random selection baseline and
other state-of-the-art active learners.
