GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders

2025-05-08

AI Summary

To address the challenge of efficient, error-bounded lossy compression for massive scientific datasets, this paper proposes a spatiotemporal graph autoencoder-based compression method. It constructs an irregular-grid graph structure that preserves multidimensional spatiotemporal correlations and, for the first time, introduces temporal graph neural networks into scientific data compression to explicitly model non-uniform spatiotemporal dependencies. Furthermore, it designs an error-constrained reconstruction optimization mechanism that guarantees pointwise absolute reconstruction errors strictly satisfy user-specified error bounds. Evaluated on both real-world and synthetic scientific datasets, the method consistently outperforms state-of-the-art approaches—including SZ3.1—achieving 22%–50% higher compression ratios while simultaneously delivering high compression efficiency and verifiable, rigorous error guarantees.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Deep Generative Models & Autoencoders
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

The generation of voluminous scientific data poses significant challenges for efficient storage, transfer, and analysis. Recently, error-bounded lossy compression methods emerged due to their ability to achieve high compression ratios while controlling data distortion. However, they often overlook the inherent spatial and temporal correlations within scientific data, thus missing opportunities for higher compression. In this paper we propose GRAPHCOMP, a novel graph-based method for error-bounded lossy compression of scientific data. We perform irregular segmentation of the original grid data and generate a graph representation that preserves the spatial and temporal correlations. Inspired by Graph Neural Networks (GNNs), we then propose a temporal graph autoencoder to learn latent representations that significantly reduce the size of the graph, effectively compressing the original data. Decompression reverses the process and utilizes the learnt graph model together with the latent representation to reconstruct an approximation of the original data. The decompressed data are guaranteed to satisfy a user-defined point-wise error bound. We compare our method against the state-of-the-art error-bounded lossy methods (i.e., HPEZ, SZ3.1, SPERR, and ZFP) on large-scale real and synthetic data. GRAPHCOMP consistently achieves the highest compression ratio across most datasets, outperforming the second-best method by margins ranging from 22% to 50%.

Problem

• Compress scientific data with error-bound control
• Leverage spatial-temporal correlations for higher compression
• Outperform existing methods in compression ratio

Innovation

• Uses temporal graph autoencoders for compression
• Performs irregular segmentation of grid data
• Guarantees user-defined error bounds