# Compressing Graphs and Indexes with Recursive Graph Bisection

arXiv: https://arxiv.org/abs/1602.08820 · HTML: https://mlsanity.com/paper/1602.08820/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2016.

## Built on by (most cited first, 8 of 8)

- [Personalized Subgraph Federated Learning](https://mlsanity.com/paper/2206.10206/) (2022)
- [Partition and Code: learning how to compress graphs](https://mlsanity.com/paper/2107.01952/) (2021)
- [Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning](https://mlsanity.com/paper/2501.00884/) (2025)
- [NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors](https://mlsanity.com/paper/2302.04570/) (2023)
- [GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs](https://mlsanity.com/paper/2401.03114/) (2024)
- [Graph Summarization via Node Grouping: A Spectral Algorithm](https://mlsanity.com/paper/2211.04169/) (2022)
- [Community-Aware Vertex Ordering for Reference-Based Graph Compression: A Cross-Encoder Empirical Study](https://mlsanity.com/paper/2605.21510/) (2026)
- [Hierarchical BM25: Lexical Search at Billion-Document Scale](https://mlsanity.com/paper/2608.00229/) (2026)

## Abstract (arXiv)

Graph reordering is a powerful technique to increase the locality of the representations of graphs, which can be helpful in several applications. We study how the technique can be used to improve compression of graphs and inverted indexes. We extend the recent theoretical model of Chierichetti et al. (KDD 2009) for graph compression, and show how it can be employed for compression-friendly reordering of social networks and web graphs and for assigning document identifiers in inverted indexes. We design and implement a novel theoretically sound reordering algorithm that is based on recursive graph bisection. Our experiments show a significant improvement of the compression rate of graph and indexes over existing heuristics. The new method is relatively simple and allows efficient parallel and distributed implementations, which is demonstrated on graphs with billions of vertices and hundreds of billions of edges.