{"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-network-approach-to-topic-models","title":"A network approach to topic models","arxiv_id":"1708.01677","date":"2017-08-04","proceeding":null,"authors":["Martin Gerlach","Tiago P. Peixoto","Eduardo G. Altmann"],"abstract":"One of the main computational and scientific challenges in the modern age is\nto extract useful information from unstructured texts. Topic models are one\npopular machine-learning approach which infers the latent topical structure of\na collection of documents. Despite their success --- in particular of its most\nwidely used variant called Latent Dirichlet Allocation (LDA) --- and numerous\napplications in sociology, history, and linguistics, topic models are known to\nsuffer from severe conceptual and practical problems, e.g. a lack of\njustification for the Bayesian priors, discrepancies with statistical\nproperties of real texts, and the inability to properly choose the number of\ntopics. Here we obtain a fresh view on the problem of identifying topical\nstructures by relating it to the problem of finding communities in complex\nnetworks. This is achieved by representing text corpora as bipartite networks\nof documents and words. By adapting existing community-detection methods --\nusing a stochastic block model (SBM) with non-parametric priors -- we obtain a\nmore versatile and principled framework for topic modeling (e.g., it\nautomatically detects the number of topics and hierarchically clusters both the\nwords and documents). The analysis of artificial and real corpora demonstrates\nthat our SBM approach leads to better topic models than LDA in terms of\nstatistical model selection. More importantly, our work shows how to formally\nrelate methods from community detection and topic modeling, opening the\npossibility of cross-fertilization between these two fields.","url_abs":"http://arxiv.org/abs/1708.01677v2","url_pdf":"http://arxiv.org/pdf/1708.01677v2.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-network-approach-to-topic-models","repo_url":"https://github.com/martingerlach/hSBM_Topicmodel","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"model-selection","task_name":"Model Selection"},{"task_slug":"sociology","task_name":"Sociology"},{"task_slug":"stochastic-block-model","task_name":"Stochastic Block Model"},{"task_slug":"topic-models","task_name":"Topic Models"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.01677","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}