{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/atom-neural-traffic-compression-with-spatio","title":"Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks","arxiv_id":"2311.05337","date":"2023-11-09","proceeding":null,"authors":["Paul Almasan","Krzysztof Rusek","Shihan Xiao","Xiang Shi","Xiangle Cheng","Albert Cabellos-Aparicio","Pere Barlet-Ros"],"abstract":"Storing network traffic data is key to efficient network management; however, it is becoming more challenging and costly due to the ever-increasing data transmission rates, traffic volumes, and connected devices. In this paper, we explore the use of neural architectures for network traffic compression. Specifically, we consider a network scenario with multiple measurement points in a network topology. Such measurements can be interpreted as multiple time series that exhibit spatial and temporal correlations induced by network topology, routing, or user behavior. We present \\textit{Atom}, a neural traffic compression method that leverages spatial and temporal correlations present in network traffic. \\textit{Atom} implements a customized spatio-temporal graph neural network design that effectively exploits both types of correlations simultaneously. The experimental results show that \\textit{Atom} can outperform GZIP's compression ratios by 50\\%-65\\% on three real-world networks.","url_abs":"https://arxiv.org/abs/2311.05337v1","url_pdf":"https://arxiv.org/pdf/2311.05337v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"atom-neural-traffic-compression-with-spatio","repo_url":"https://github.com/bnn-upc/atom_neural_traffic_compression","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}