# Recent Advances in Graph Partitioning

arXiv: https://arxiv.org/abs/1311.3144 · HTML: https://mlsanity.com/paper/1311.3144/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2013.

## Built on by (most cited first, 25 of 25)

- [A Generalization of ViT/MLP-Mixer to Graphs](https://mlsanity.com/paper/2212.13350/) (2022)
- [Decentralized Training of Foundation Models in Heterogeneous Environments](https://mlsanity.com/paper/2206.01288/) (2022)
- [BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling](https://mlsanity.com/paper/2203.10983/) (2022)
- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Survey on Graph Neural Network Acceleration: An Algorithmic Perspective](https://mlsanity.com/paper/2202.04822/) (2022)
- [More Recent Advances in (Hyper)Graph Partitioning](https://mlsanity.com/paper/2205.13202/) (2022)
- [A Survey on the Densest Subgraph Problem and Its Variants](https://mlsanity.com/paper/2303.14467/) (2023)
- [A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management](https://mlsanity.com/paper/2306.02806/) (2023)
- [Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning](https://mlsanity.com/paper/2310.10374/) (2023)
- [Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning](https://mlsanity.com/paper/2404.11384/) (2024)
- [Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning](https://mlsanity.com/paper/2102.10169/) (2021)
- [MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs](https://mlsanity.com/paper/2410.22697/) (2024)
- [Sparsity-Aware Communication for Distributed Graph Neural Network Training](https://mlsanity.com/paper/2504.04673/) (2025)
- [Distributed Graph Embedding with Information-Oriented Random Walks](https://mlsanity.com/paper/2303.15702/) (2023)
- [Counterfactual Explanations for Graph Classification Through the Lenses of Density](https://mlsanity.com/paper/2307.14849/) (2023)
- [Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning](https://mlsanity.com/paper/2505.13994/) (2025)
- [Sampling-based techniques for designing school boundaries](https://mlsanity.com/paper/2206.03703/) (2022)
- [Memetic algorithms for Spatial Partitioning problems](https://mlsanity.com/paper/2208.02867/) (2022)
- [A Bayesian Approach To Graph Partitioning](https://mlsanity.com/paper/2204.12927/) (2022)
- [Partition Pooling for Convolutional Graph Network Applications in Particle Physics](https://mlsanity.com/paper/2208.05952/) (2022)
- [Direct Comparative Analysis of Nature-inspired Optimization Algorithms on Community Detection Problem in Social Networks](https://mlsanity.com/paper/2212.10797/) (2022)
- [Optimal Clustering with Dependent Costs in Bayesian Networks](https://mlsanity.com/paper/2308.03970/) (2023)
- [Minimum Bisection Problem: Machine Learning-Based Penalty Parameter Tuning for Optimization on Quantum Annealers](https://mlsanity.com/paper/2509.19005/) (2025)
- [Surrogate Graph Partitioning for Spatial Prediction](https://mlsanity.com/paper/2510.07832/) (2025)
- [Interpretable Graph Partitioning for Spatial Prediction](https://mlsanity.com/paper/2610.16063/) (2026)

## Abstract (arXiv)

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions.