[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-156019-105":59,"doc-detail-156019-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","topic-models-for-corpora-and-graphs","Topic Models for Corpora and Graphs","","This document explores topic modeling techniques applicable to both corpora (collections of text) and graphs (networks of interconnected nodes). It introduces the motivation behind modeling social graphs, which exhibit characteristics of both randomness and structure, such as small diameters, giant connected components, homophily, and scale-free degree distributions. The document delves into stochastic block models, also known as block-stochastic matrices, where nodes are divided into blocks, and connections between nodes within or between blocks are made with specific probabilities. A key inference question is posed: can this model be inverted to infer the block structure from a given graph? The presentation reviews essential text models, including the Mixture of Multinomials with Expectation-Maximization (EM) and Latent Dirichlet Allocation (LDA) implemented via Gibbs sampling or variational EM. It then transitions to block models and their inference, followed by an examination of Mixed-Membership Block Models and Multinomial Block Models with inference using Gibbs sampling. A review of supervised Naïve Bayes classification is also provided, detailing the Multinomial Naïve Bayes model and its learning process through maximizing the log-likelihood of observed variables, highlighting that this results in a convex function, ensuring a global optimum. The underlying mathematical formulations and computational steps for these models are presented, suggesting a focus on both theoretical underpinnings and practical implementation within the field of network and text analysis.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/topic-models-for-corpora-and-graphs/156019/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/topic-models-for-corpora-and-graphs/156019.png","ImageObject",300,407,{"name":92,"@type":93},"Arica Lee","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-28",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What are stochastic block models?","Question",{"text":113,"@type":114},"Stochastic block models, also known as block-stochastic matrices, are a type of model for graphs where nodes are divided into blocks, and connections between nodes within or between these blocks are made with specific probabilities.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What is the main inference question addressed regarding block models?",{"text":118,"@type":114},"The main inference question is whether it's possible to infer the block structure of a graph by inverting the stochastic block model, i.e., learning the probabilities and latent node-block mappings from the graph itself.",{"name":120,"@type":111,"acceptedAnswer":121},"What text models are reviewed in the document?",{"text":122,"@type":114},"The document reviews text models such as the Mixture of Multinomials with EM, and Latent Dirichlet Allocation (LDA) implemented using Gibbs sampling or variational EM.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},156019,1787942816,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},8796096645457,"https://ap-avatar.wpscdn.com/avatar/800003749518d68ffe3?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345340919836971","Topic models for corpora and for graphs  \nNetwork Datasets  \n•UBMCBlog  \n•AGBlog  \n•MSPBlog  \n•Cora  \n•Citeseer  \nMotivation  \n• Social graphs seem to have  \n– some aspects of randomness  \n• small diameter, giant connected components, ..  \n– some structure  \n• homophily, scale-free degree dist?  \n• How do you model it?  \nMore terms  \n• “Stochastic block model”, aka “Blockstochastic matrix”:  \n– Draw ni nodes in block i  \n– With probability pij, connect pairs (u, v) where u is in block i, v is in block j  \n– Special, simple case: pii =qi, and pij=s for all i≠j  \n• Question: can you Lit this model to a graph?  \n– Lind each pij and latent nodeàblock mapping  \nNot? football  \nNot? books  \nOutline  \n• Stochastic block models & inference question  \n• Review of text models  \n– Mixture of multinomials & EM  \n– LDA and Gibbs (or variational EM)  \n• Block models and inference  \n• Mixed-membership block models  \n• Multinomial block models and inference w/ Gibbs  \nReview – supervised Naïve Bayes  \n• Naïve Bayes Model: Compact representation  \nπ  \n\n| | M |\n| --- | --- |\n\nβ  \nπ  \n\n| | M |\n| --- | --- |\n\nβ  \nReview – supervised Naïve Bayes  \n• Multinomial Naïve Bayes  \nπ  \nC  \nW2  \nW3 …. .  \n  \nM  \nβ  \n• For each document d = 1, !, M  \n• Generate Cd ~ Mult( | π )  \n• For each position n = 1, !, Nd  \n• Generate wn ~ Mult( |β,Cd)  \nReview – supervised Naïve Bayes  \n• Multinomial naïve Bayes: Learning  \n– Maximize the log-likelihood of observed variables w.r.t. the parameters:  \n• Convex function: global optimum  \n• Solution:","cbCaitK8WLEK3nRg","https://ap.wps.com/l/cbCaitK8WLEK3nRg","pdf",6195131,64,"English","# Topic Models for Corpora and Graphs\n## Network Datasets\n## Motivation\n## More terms\n## Outline\n## Review – supervised Naïve Bayes\n## Review – supervised Naïve Bayes\n## Review – supervised Naïve Bayes","[{\"question\":\"What are stochastic block models?\",\"answer\":\"Stochastic block models, also known as block-stochastic matrices, are a type of model for graphs where nodes are divided into blocks, and connections between nodes within or between these blocks are made with specific probabilities.\"},{\"question\":\"What is the main inference question addressed regarding block models?\",\"answer\":\"The main inference question is whether it's possible to infer the block structure of a graph by inverting the stochastic block model, i.e., learning the probabilities and latent node-block mappings from the graph itself.\"},{\"question\":\"What text models are reviewed in the document?\",\"answer\":\"The document reviews text models such as the Mixture of Multinomials with EM, and Latent Dirichlet Allocation (LDA) implemented using Gibbs sampling or variational EM.\"}]","Topic Models for Corpora and Graphs | PDF",161]