# Model-Change Active Learning in Graph-Based Semi-Supervised Learning

arXiv: https://arxiv.org/abs/2110.07739 · HTML: https://mlsanity.com/paper/2110.07739/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates](https://mlsanity.com/paper/2006.11184/) (2020)
- [Rethinking deep active learning: Using unlabeled data at model training](https://mlsanity.com/paper/1911.08177/) (2019)
- [S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification](https://mlsanity.com/paper/1506.08760/) (2015)
- [MaxiMin Active Learning in Overparameterized Model Classes}](https://mlsanity.com/paper/1905.12782/) (2019)
- [Diffusion-based Deep Active Learning](https://mlsanity.com/paper/2003.10339/) (2020)
- [Learning by Active Nonlinear Diffusion](https://mlsanity.com/paper/1905.12989/) (2019)
- [Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods](https://mlsanity.com/paper/1906.07658/) (2019)
- [Posterior Consistency of Semi-Supervised Regression on Graphs](https://mlsanity.com/paper/2007.12809/) (2020)
- [Graph-Based Active Learning: A New Look at Expected Error Minimization](https://mlsanity.com/paper/1609.00845/) (2016)
- [Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations](https://mlsanity.com/paper/2007.11126/) (2020)

## Built on by (most cited first, 8 of 8)

- [Graph-based Active Learning for Semi-supervised Classification of SAR Data](https://mlsanity.com/paper/2204.00005/) (2022)
- [Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning](https://mlsanity.com/paper/2210.15786/) (2022)
- [Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets](https://mlsanity.com/paper/2307.10495/) (2023)
- [Consistency of semi-supervised learning, stochastic tug-of-war games, and the p-Laplacian](https://mlsanity.com/paper/2401.07463/) (2024)
- [MALADY: Multiclass Active Learning with Auction Dynamics on Graphs](https://mlsanity.com/paper/2409.09475/) (2024)
- [Higher-Order Regularization Learning on Hypergraphs](https://mlsanity.com/paper/2510.26533/) (2025)
- [A signal separation view of classification](https://mlsanity.com/paper/2509.24140/) (2025)
- [Topology-Aware Active Learning on Graphs](https://mlsanity.com/paper/2510.25892/) (2025)

## Abstract (arXiv)

Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify which points to label to best improve performance while limiting the number of new labels. "Model Change" active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s). We pair this idea with graph-based semi-supervised learning methods, that use the spectrum of the graph Laplacian matrix, which can be truncated to avoid prohibitively large computational and storage costs. We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution. We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.