# Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics

arXiv: https://arxiv.org/abs/1912.08950 · HTML: https://mlsanity.com/paper/1912.08950/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Motif Prediction with Graph Neural Networks](https://mlsanity.com/paper/2106.00761/) (2021)
- [Neural Graph Databases](https://mlsanity.com/paper/2209.09732/) (2022)
- [GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra](https://mlsanity.com/paper/2103.03653/) (2021)
- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)
- [Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study](https://mlsanity.com/paper/2409.11129/) (2024)
- [GraphSeek: Next-Generation Graph Analytics with LLMs](https://mlsanity.com/paper/2602.11052/) (2026)
- [A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs](https://mlsanity.com/paper/2403.13286/) (2024)
- [Higher-Order Graph Databases](https://mlsanity.com/paper/2506.19661/) (2025)
- [CGS: Configurable Graph Summarization with Bounded Neighborhood Loss and Query Support](https://mlsanity.com/paper/2607.10969/) (2026)

## Abstract (arXiv)

We propose Slim Graph: the first programming model and framework for practical lossy graph compression that facilitates high-performance approximate graph processing, storage, and analytics. Slim Graph enables the developer to express numerous compression schemes using small and programmable compression kernels that can access and modify local parts of input graphs. Such kernels are executed in parallel by the underlying engine, isolating developers from complexities of parallel programming. Our kernels implement novel graph compression schemes that preserve numerous graph properties, for example connected components, minimum spanning trees, or graph spectra. Finally, Slim Graph uses statistical divergences and other metrics to analyze the accuracy of lossy graph compression. We illustrate both theoretically and empirically that Slim Graph accelerates numerous graph algorithms, reduces storage used by graph datasets, and ensures high accuracy of results. Slim Graph may become the common ground for developing, executing, and analyzing emerging lossy graph compression schemes.