{"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/scalable-models-for-computing-hierarchies-in","title":"Scalable Models for Computing Hierarchies in Information Networks","arxiv_id":"1601.00626","date":"2016-01-04","proceeding":null,"authors":["Baoxu Shi","Tim Weninger"],"abstract":"Information hierarchies are organizational structures that often used to\norganize and present large and complex information as well as provide a\nmechanism for effective human navigation. Fortunately, many statistical and\ncomputational models exist that automatically generate hierarchies; however,\nthe existing approaches do not consider linkages in information {\\em networks}\nthat are increasingly common in real-world scenarios. Current approaches also\ntend to present topics as an abstract probably distribution over words, etc\nrather than as tangible nodes from the original network. Furthermore, the\nstatistical techniques present in many previous works are not yet capable of\nprocessing data at Web-scale. In this paper we present the Hierarchical\nDocument Topic Model (HDTM), which uses a distributed vertex-programming\nprocess to calculate a nonparametric Bayesian generative model. Experiments on\nthree medium size data sets and the entire Wikipedia dataset show that HDTM can\ninfer accurate hierarchies even over large information networks.","url_abs":"http://arxiv.org/abs/1601.00626v1","url_pdf":"http://arxiv.org/pdf/1601.00626v1.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":"scalable-models-for-computing-hierarchies-in","repo_url":"https://github.com/nddsg/HDTM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}