# Graph Partition Neural Networks for Semi-Supervised Classification

arXiv: https://arxiv.org/abs/1803.06272 · HTML: https://mlsanity.com/paper/1803.06272/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

## Built on by (most cited first, 17 of 17)

- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm](https://mlsanity.com/paper/2203.00638/) (2022)
- [Large-scale graph representation learning with very deep GNNs and self-supervision](https://mlsanity.com/paper/2107.09422/) (2021)
- [GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy](https://mlsanity.com/paper/2104.10569/) (2021)
- [Multiscale Laplacian Learning](https://mlsanity.com/paper/2109.03718/) (2021)
- [Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning](https://mlsanity.com/paper/2408.14964/) (2024)
- [Permutation-Invariant Graph Partitioning:How Graph Neural Networks Capture Structural Interactions?](https://mlsanity.com/paper/2312.08671/) (2023)
- [Beyond the Known: Novel Class Discovery for Open-world Graph Learning](https://mlsanity.com/paper/2403.19907/) (2024)
- [Partitioned Neural Network Training via Synthetic Intermediate Labels](https://mlsanity.com/paper/2403.11204/) (2024)
- [Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence](https://mlsanity.com/paper/2605.18893/) (2026)
- [Deep Dive into Semi-Supervised ELBO for Improving Classification Performance](https://mlsanity.com/paper/2108.12734/) (2021)
- [gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs](https://mlsanity.com/paper/2210.11601/) (2022)
- [Fast and Effective GNN Training through Sequences of Random Path Graphs](https://mlsanity.com/paper/2306.04828/) (2023)
- [Large Scale Training of Graph Neural Networks for Optimal Markov-Chain Partitioning Using the Kemeny Constant](https://mlsanity.com/paper/2312.14847/) (2023)
- [Geometric 2D Scene Graph Generation](https://mlsanity.com/paper/2607.22325/) (2026)
- [Link prediction on multi-relational graphs from an influence propagation perspective](https://mlsanity.com/paper/2608.05016/) (2026)

## Abstract (arXiv)

We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we experiment with several partitioning algorithms and also propose a novel variant for fast processing of large scale graphs. We extensively test our model on a variety of semi-supervised node classification tasks. Experimental results indicate that GPNNs are either superior or comparable to state-of-the-art methods on a wide variety of datasets for graph-based semi-supervised classification. We also show that GPNNs can achieve similar performance as standard GNNs with fewer propagation steps.