# Graph-based Semi-supervised Local Clustering with Few Labeled Nodes

arXiv: https://arxiv.org/abs/2211.11114 · HTML: https://mlsanity.com/paper/2211.11114/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 75 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Community Detection and Stochastic Block Models](https://mlsanity.com/paper/1703.10146/) (2017)
- [Graph Clustering with Graph Neural Networks](https://mlsanity.com/paper/2006.16904/) (2020)
- [Hierarchical Clustering: Objective Functions and Algorithms](https://mlsanity.com/paper/1704.02147/) (2017)
- [Heat kernel based community detection](https://mlsanity.com/paper/1403.3148/) (2014)
- [Structured Graph Learning for Clustering and Semi-supervised Classification](https://mlsanity.com/paper/2008.13429/) (2020)
- [p-Norm Flow Diffusion for Local Graph Clustering](https://mlsanity.com/paper/2005.09810/) (2020)
- [Variational Perspective on Local Graph Clustering](https://mlsanity.com/paper/1602.01886/) (2016)
- [Recovering communities in the general stochastic block model without knowing the parameters](https://mlsanity.com/paper/1506.03729/) (2015)
- [Capacity Releasing Diffusion for Speed and Locality](https://mlsanity.com/paper/1706.05826/) (2017)
- [Power Weighted Shortest Paths for Clustering Euclidean Data](https://mlsanity.com/paper/1905.13345/) (2019)
- [Flow-Based Algorithms for Local Graph Clustering](https://mlsanity.com/paper/1307.2855/) (2013)
- [Flow-Based Local Graph Clustering with Better Seed Set Inclusion](https://mlsanity.com/paper/1811.12280/) (2018)
- [A Simple and Strongly-Local Flow-Based Method for Cut Improvement](https://mlsanity.com/paper/1605.08490/) (2016)
- [A Compressed Sensing Based Least Squares Approach to Semi-supervised Local Cluster Extraction](https://mlsanity.com/paper/2202.02904/) (2022)
- [A testing based extraction algorithm for identifying significant communities in networks](https://mlsanity.com/paper/1308.0777/) (2013)
- [A Short Introduction to Local Graph Clustering Methods and Software](https://mlsanity.com/paper/1810.07324/) (2018)

## Built on by (most cited first, 3 of 3)

- [Local Clustering for Lung Cancer Image Classification via Sparse Solution Technique](https://mlsanity.com/paper/2407.08800/) (2024)
- [Advancing Local Clustering on Graphs via Compressive Sensing: Semi-supervised and Unsupervised Methods](https://mlsanity.com/paper/2504.19419/) (2025)
- [Scalable Graph Coreset Selection via Greedy Sampling](https://mlsanity.com/paper/2607.27602/) (2026)

## Abstract (arXiv)

Local clustering aims at extracting a local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be thought as a sparse solution to a linear system. In this paper, we apply this idea based on two pioneering works under the same framework and propose a new semi-supervised local clustering approach using only few labeled nodes. Our approach improves the existing works by making the initial cut to be the entire graph and hence overcomes a major limitation of the existing works, which is the low quality of initial cut. Extensive experimental results on various datasets demonstrate the effectiveness of our approach.