# Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation

arXiv: https://arxiv.org/abs/1512.01752 · HTML: https://mlsanity.com/paper/1512.01752/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2015.

## Built on by (most cited first, 7 of 7)

- [GraphHop: An Enhanced Label Propagation Method for Node Classification](https://mlsanity.com/paper/2101.02326/) (2021)
- [MLOps Spanning Whole Machine Learning Life Cycle: A Survey](https://mlsanity.com/paper/2304.07296/) (2023)
- [Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images](https://mlsanity.com/paper/2102.13517/) (2021)
- [A Clustering-based Framework for Classifying Data Streams](https://mlsanity.com/paper/2106.11823/) (2021)
- [Large-Scale Graph Building in Dynamic Environments: Low Latency and High Quality](https://mlsanity.com/paper/2507.10139/) (2025)
- [SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification](https://mlsanity.com/paper/2508.09544/) (2025)
- [DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning](https://mlsanity.com/paper/2604.06596/) (2026)

## Abstract (arXiv)

Traditional graph-based semi-supervised learning (SSL) approaches, even though widely applied, are not suited for massive data and large label scenarios since they scale linearly with the number of edges |E| and distinct labels m. To deal with the large label size problem, recent works propose sketch-based methods to approximate the distribution on labels per node thereby achieving a space reduction from O(m) to O(log m), under certain conditions. In this paper, we present a novel streaming graph-based SSL approximation that captures the sparsity of the label distribution and ensures the algorithm propagates labels accurately, and further reduces the space complexity per node to O(1). We also provide a distributed version of the algorithm that scales well to large data sizes. Experiments on real-world datasets demonstrate that the new method achieves better performance than existing state-of-the-art algorithms with significant reduction in memory footprint. We also study different graph construction mechanisms for natural language applications and propose a robust graph augmentation strategy trained using state-of-the-art unsupervised deep learning architectures that yields further significant quality gains.