{"id":"https://openalex.org/W3012593603","doi":"https://doi.org/10.1145/3366423.3380102","title":"Graph Attention Topic Modeling Network","display_name":"Graph Attention Topic Modeling Network","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3012593603","doi":"https://doi.org/10.1145/3366423.3380102","mag":"3012593603"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380102","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380102","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380102","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003769188","display_name":"Liang Yang","orcid":"https://orcid.org/0000-0001-6291-4359"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liang Yang","raw_affiliation_strings":["Hebei University of Technology"],"affiliations":[{"raw_affiliation_string":"Hebei University of Technology","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101421950","display_name":"Fan Wu","orcid":"https://orcid.org/0009-0006-9264-7646"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Wu","raw_affiliation_strings":["Hebei University of Technology"],"affiliations":[{"raw_affiliation_string":"Hebei University of Technology","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109440399","display_name":"Junhua Gu","orcid":"https://orcid.org/0009-0003-0982-417X"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junhua Gu","raw_affiliation_strings":["Hebei University of Technology"],"affiliations":[{"raw_affiliation_string":"Hebei University of Technology","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101632143","display_name":"Chuan Wang","orcid":"https://orcid.org/0000-0001-5143-1714"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Wang","raw_affiliation_strings":["Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068837264","display_name":"Xiaochun Cao","orcid":"https://orcid.org/0000-0001-7141-708X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaochun Cao","raw_affiliation_strings":["Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112574959","display_name":"Di Jin","orcid":"https://orcid.org/0009-0001-4646-5220"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Jin","raw_affiliation_strings":["Tianjin University"],"affiliations":[{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030187602","display_name":"Yuanfang Guo","orcid":"https://orcid.org/0000-0003-4592-8083"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanfang Guo","raw_affiliation_strings":["Beihang University"],"affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5003769188"],"corresponding_institution_ids":["https://openalex.org/I184843921"],"apc_list":null,"apc_paid":null,"fwci":4.6632,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.95608032,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"144","last_page":"154"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.722042441368103},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6959484219551086},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6818068027496338},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.650298535823822},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5879068374633789},{"id":"https://openalex.org/keywords/amortized-analysis","display_name":"Amortized analysis","score":0.5577977895736694},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5035263895988464},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5023751258850098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47993236780166626},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4565006196498871},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.4107775092124939},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37148213386535645},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3654387593269348},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3608127534389496},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27119120955467224},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.2533111870288849}],"concepts":[{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.722042441368103},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6959484219551086},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6818068027496338},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.650298535823822},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5879068374633789},{"id":"https://openalex.org/C142417499","wikidata":"https://www.wikidata.org/wiki/Q331716","display_name":"Amortized analysis","level":3,"score":0.5577977895736694},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5035263895988464},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5023751258850098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47993236780166626},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4565006196498871},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.4107775092124939},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37148213386535645},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3654387593269348},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3608127534389496},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27119120955467224},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2533111870288849},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3366423.3380102","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380102","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380102","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380102","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1506806321","https://openalex.org/W1516111018","https://openalex.org/W1854884267","https://openalex.org/W1880262756","https://openalex.org/W1909320841","https://openalex.org/W1959608418","https://openalex.org/W1972675431","https://openalex.org/W2026487812","https://openalex.org/W2049633694","https://openalex.org/W2052261215","https://openalex.org/W2067438047","https://openalex.org/W2107743791","https://openalex.org/W2112050062","https://openalex.org/W2125031621","https://openalex.org/W2130339025","https://openalex.org/W2135029798","https://openalex.org/W2137735870","https://openalex.org/W2145768976","https://openalex.org/W2147152072","https://openalex.org/W2151383095","https://openalex.org/W2178725228","https://openalex.org/W2238728730","https://openalex.org/W2250533720","https://openalex.org/W2250539671","https://openalex.org/W2250753706","https://openalex.org/W2251012068","https://openalex.org/W2514450663","https://openalex.org/W2516087440","https://openalex.org/W2519887557","https://openalex.org/W2621133045","https://openalex.org/W2702896255","https://openalex.org/W2731449405","https://openalex.org/W2740139069","https://openalex.org/W2742276251","https://openalex.org/W2744007523","https://openalex.org/W2790504398","https://openalex.org/W2803244426","https://openalex.org/W2887092413","https://openalex.org/W2911250003","https://openalex.org/W2911286998","https://openalex.org/W2943847262","https://openalex.org/W2950133940","https://openalex.org/W2952478253","https://openalex.org/W2953060237","https://openalex.org/W2961152326","https://openalex.org/W2962943175","https://openalex.org/W2963285578","https://openalex.org/W2963403868","https://openalex.org/W2963773425","https://openalex.org/W2963858333","https://openalex.org/W2964168257","https://openalex.org/W2964308564","https://openalex.org/W2966398094","https://openalex.org/W2966461190","https://openalex.org/W2966567766","https://openalex.org/W2972209102","https://openalex.org/W3003795821","https://openalex.org/W3003874400","https://openalex.org/W4233135949"],"related_works":["https://openalex.org/W2154587273","https://openalex.org/W2251863249","https://openalex.org/W3046324450","https://openalex.org/W2894783287","https://openalex.org/W2151373442","https://openalex.org/W2103628173","https://openalex.org/W1545527235","https://openalex.org/W2096610504","https://openalex.org/W2979208245","https://openalex.org/W2171278750"],"abstract_inverted_index":{"Existing":[0],"topic":[1,70,155,286,297],"modeling":[2],"approaches":[3],"possess":[4],"several":[5],"issues,":[6],"including":[7],"the":[8,17,21,46,53,63,72,80,86,90,94,104,112,120,123,127,154,162,190,197,225,241,249,253,260,263,268,274,292,302,309],"overfitting":[9,47],"issue":[10,48],"of":[11,19,49,62,75,82,88,96,111,119,122,130,157,193,201,248,262,294],"Probablistic":[12],"Latent":[13,28],"Semantic":[14],"Indexing":[15],"(pLSI),":[16],"failure":[18],"capturing":[20],"rich":[22],"topical":[23],"correlations":[24],"among":[25],"topics":[26],"in":[27,66],"Dirichlet":[29,64],"Allocation":[30],"(LDA),":[31],"and":[32,117,135,220,252,278],"high":[33],"inference":[34,55,92,95,132,192],"complexity.":[35],"In":[36],"this":[37],"paper,":[38],"we":[39],"provide":[40],"a":[41,100,140,177,207,232],"new":[42],"method":[43],"to":[44,133,152,161,216],"overcome":[45,126],"pLSI":[50,167],"by":[51],"using":[52],"amortized":[54,91,131,203],"with":[56,99],"word":[57,218,221,237,242,250,275,279,311],"embedding":[58],"as":[59,171,189,231,246,259],"input,":[60],"instead":[61],"prior":[65],"LDA.":[67],"For":[68],"generative":[69],"model,":[71],"large":[73],"number":[74,87,110],"free":[76],"latent":[77,97],"variables":[78],"is":[79,115,150,229,244,257],"root":[81],"overfitting.":[83],"To":[84,125],"reduce":[85],"parameters,":[89],"replaces":[93],"variable":[98],"function":[101],"which":[102,195,239],"possesses":[103],"shared":[105,113],"(amortized)":[106],"learnable":[107],"parameters.":[108],"The":[109],"parameters":[114],"fixed":[116],"independent":[118,134],"scale":[121],"corpus.":[124],"limited":[128],"application":[129],"identically":[136],"distributed":[137],"(i.i.d)":[138],"data,":[139],"novel":[141,208],"graph":[142,182,211,234,271],"neural":[143],"network,":[144],"Graph":[145],"Attention":[146],"TOpic":[147],"Network":[148],"(GATON),":[149],"proposed":[151],"model":[153,174],"structure":[156,277],"non-i.i.d":[158],"documents":[159],"according":[160],"following":[163],"two":[164],"observations.":[165],"First,":[166],"can":[168,186],"be":[169,187],"interpreted":[170],"stochastic":[172],"block":[173],"(SBM)":[175],"on":[176,267,296],"specific":[178],"bi-partite":[179,233],"graph.":[180],"Second,":[181],"attention":[183],"network":[184],"(GAT)":[185],"explained":[188],"semi-amortized":[191],"SBM,":[194],"relaxes":[196],"i.i.d":[198],"data":[199],"assumption":[200],"vanilla":[202],"inference.":[204],"GATON":[205,295],"provides":[206],"scheme,":[209,215],"i.e.":[210],"convolution":[212,272],"operation":[213],"based":[214],"integrate":[217],"similarity":[219,280],"co-occurrence":[222,276],"structure.":[223],"Specifically,":[224],"bag-of-words":[226],"document":[227,264,303],"representation":[228],"modeled":[230,245],"topology.":[235],"Meanwhile,":[236],"embedding,":[238],"captures":[240],"similarity,":[243],"attribute":[247,261],"node":[251],"term":[254],"frequency":[255],"vector":[256],"adopted":[258],"node.":[265],"Based":[266],"weighted":[269],"(attention)":[270],"operation,":[273],"patterns":[281],"are":[282],"seamlessly":[283],"integrated":[284],"for":[285],"identification.":[287],"Extensive":[288],"experiments":[289],"demonstrate":[290],"that":[291],"effectiveness":[293],"identification":[298],"not":[299],"only":[300],"benefits":[301],"classification,":[304],"but":[305],"also":[306],"significantly":[307],"refines":[308],"input":[310],"embedding.":[312]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":3}],"updated_date":"2026-03-17T09:09:15.849793","created_date":"2025-10-10T00:00:00"}