{"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/network-inference-via-the-time-varying","title":"Network Inference via the Time-Varying Graphical Lasso","arxiv_id":"1703.01958","date":"2017-03-06","proceeding":null,"authors":["David Hallac","Youngsuk Park","Stephen Boyd","Jure Leskovec"],"abstract":"Many important problems can be modeled as a system of interconnected\nentities, where each entity is recording time-dependent observations or\nmeasurements. In order to spot trends, detect anomalies, and interpret the\ntemporal dynamics of such data, it is essential to understand the relationships\nbetween the different entities and how these relationships evolve over time. In\nthis paper, we introduce the time-varying graphical lasso (TVGL), a method of\ninferring time-varying networks from raw time series data. We cast the problem\nin terms of estimating a sparse time-varying inverse covariance matrix, which\nreveals a dynamic network of interdependencies between the entities. Since\ndynamic network inference is a computationally expensive task, we derive a\nscalable message-passing algorithm based on the Alternating Direction Method of\nMultipliers (ADMM) to solve this problem in an efficient way. We also discuss\nseveral extensions, including a streaming algorithm to update the model and\nincorporate new observations in real time. Finally, we evaluate our TVGL\nalgorithm on both real and synthetic datasets, obtaining interpretable results\nand outperforming state-of-the-art baselines in terms of both accuracy and\nscalability.","url_abs":"http://arxiv.org/abs/1703.01958v2","url_pdf":"http://arxiv.org/pdf/1703.01958v2.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":"abstracts"},"code_links":[{"paper_slug":"network-inference-via-the-time-varying","repo_url":"https://github.com/ams129/TVGL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"network-inference-via-the-time-varying","repo_url":"https://github.com/tpetaja1/tvgl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.01958","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}