# Active Community Detection with Maximal Expected Model Change

arXiv: https://arxiv.org/abs/1801.05856 · HTML: https://mlsanity.com/paper/1801.05856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [Query-Limited Community Recovery in Stochastic Block Models](https://mlsanity.com/paper/2606.02055/) (2026)

## Abstract (arXiv)

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.