{"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/block-structure-based-time-series-models-for","title":"Block-Structure Based Time-Series Models For Graph Sequences","arxiv_id":"1804.08796","date":"2018-04-24","proceeding":null,"authors":["Mehrnaz Amjadi","Theja Tulabandhula"],"abstract":"Although the computational and statistical trade-off for modeling single\ngraphs, for instance, using block models is relatively well understood,\nextending such results to sequences of graphs has proven to be difficult. In\nthis work, we take a step in this direction by proposing two models for graph\nsequences that capture: (a) link persistence between nodes across time, and (b)\ncommunity persistence of each node across time. In the first model, we assume\nthat the latent community of each node does not change over time, and in the\nsecond model we relax this assumption suitably. For both of these proposed\nmodels, we provide statistically and computationally efficient inference\nalgorithms, whose unique feature is that they leverage community detection\nmethods that work on single graphs. We also provide experimental results\nvalidating the suitability of our models and methods on synthetic and real\ninstances.","url_abs":"http://arxiv.org/abs/1804.08796v2","url_pdf":"http://arxiv.org/pdf/1804.08796v2.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":"block-structure-based-time-series-models-for","repo_url":"https://github.com/thejat/dynamic-network-growth-models","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"community-detection","task_name":"Community Detection"},{"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":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}