{"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/a-physical-model-for-efficient-ranking-in","title":"A physical model for efficient ranking in networks","arxiv_id":"1709.09002","date":"2017-09-03","proceeding":null,"authors":["Caterina De Bacco","Daniel B. Larremore","Cristopher Moore"],"abstract":"We present a physically-inspired model and an efficient algorithm to infer\nhierarchical rankings of nodes in directed networks. It assigns real-valued\nranks to nodes rather than simply ordinal ranks, and it formalizes the\nassumption that interactions are more likely to occur between individuals with\nsimilar ranks. It provides a natural statistical significance test for the\ninferred hierarchy, and it can be used to perform inference tasks such as\npredicting the existence or direction of edges. The ranking is obtained by\nsolving a linear system of equations, which is sparse if the network is; thus\nthe resulting algorithm is extremely efficient and scalable. We illustrate\nthese findings by analyzing real and synthetic data, including datasets from\nanimal behavior, faculty hiring, social support networks, and sports\ntournaments. We show that our method often outperforms a variety of others, in\nboth speed and accuracy, in recovering the underlying ranks and predicting edge\ndirections.","url_abs":"http://arxiv.org/abs/1709.09002v4","url_pdf":"http://arxiv.org/pdf/1709.09002v4.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":"a-physical-model-for-efficient-ranking-in","repo_url":"https://github.com/cdebacco/SpringRank","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-physical-model-for-efficient-ranking-in","repo_url":"https://github.com/cdebacco/dynspringrank","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}