# Semi-Supervised Clustering with Inaccurate Pairwise Annotations

arXiv: https://arxiv.org/abs/2104.02146 · HTML: https://mlsanity.com/paper/2104.02146/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [Community Detection and Stochastic Block Models](https://mlsanity.com/paper/1703.10146/) (2017)
- [Consistency of spectral clustering in stochastic block models](https://mlsanity.com/paper/1312.2050/) (2013)
- [A Review of Stochastic Block Models and Extensions for Graph Clustering](https://mlsanity.com/paper/1903.00114/) (2019)
- [Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel](https://mlsanity.com/paper/1309.4111/) (2013)
- [Bayesian stochastic blockmodeling](https://mlsanity.com/paper/1705.10225/) (2017)
- [Robust and computationally feasible community detection in the presence of arbitrary outlier nodes](https://mlsanity.com/paper/1404.6000/) (2014)
- [Community detection with node attributes in multilayer networks](https://mlsanity.com/paper/2004.09160/) (2020)
- [Stochastic Block Models with Multiple Continuous Attributes](https://mlsanity.com/paper/1803.02726/) (2018)
- [Community detection in networks: Modularity optimization and maximum likelihood are equivalent](https://mlsanity.com/paper/1606.02319/) (2016)
- [HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering](https://mlsanity.com/paper/1804.09813/) (2018)
- [k-means as a variational EM approximation of Gaussian mixture models](https://mlsanity.com/paper/1704.04812/) (2017)
- [Assortative-Constrained Stochastic Block Models](https://mlsanity.com/paper/2004.11890/) (2020)

## Built on by (most cited first, 2 of 2)

- [Active clustering for labeling training data](https://mlsanity.com/paper/2110.14521/) (2021)
- [Memetic Differential Evolution Methods for Semi-Supervised Clustering](https://mlsanity.com/paper/2403.04322/) (2024)

## Abstract (arXiv)

Pairwise relational information is a useful way of providing partial supervision in domains where class labels are difficult to acquire. This work presents a clustering model that incorporates pairwise annotations in the form of must-link and cannot-link relations and considers possible annotation inaccuracies (i.e., a common setting when experts provide pairwise supervision). We propose a generative model that assumes Gaussian-distributed data samples along with must-link and cannot-link relations generated by stochastic block models. We adopt a maximum-likelihood approach and demonstrate that, even when supervision is weak and inaccurate, accounting for relational information significantly improves clustering performance. Relational information also helps to detect meaningful groups in real-world datasets that do not fit the original data-distribution assumptions. Additionally, we extend the model to integrate prior knowledge of experts' accuracy and discuss circumstances in which the use of this knowledge is beneficial.