{"id":"https://openalex.org/W2962946486","doi":"https://doi.org/10.1609/aaai.v33i01.33017370","title":"Graph Convolutional Networks for Text Classification","display_name":"Graph Convolutional Networks for Text Classification","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2962946486","doi":"https://doi.org/10.1609/aaai.v33i01.33017370","mag":"2962946486"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v33i01.33017370","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33017370","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4725/4603","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4725/4603","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101613727","display_name":"Liang Yao","orcid":"https://orcid.org/0000-0002-8637-0760"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liang Yao","raw_affiliation_strings":["Northwestern University"],"affiliations":[{"raw_affiliation_string":"Northwestern University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074672910","display_name":"Chengsheng Mao","orcid":"https://orcid.org/0000-0002-1515-9626"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengsheng Mao","raw_affiliation_strings":["Northwestern University"],"affiliations":[{"raw_affiliation_string":"Northwestern University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100452550","display_name":"Yuan Luo","orcid":"https://orcid.org/0000-0003-0195-7456"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan Luo","raw_affiliation_strings":["Northwestern University"],"affiliations":[{"raw_affiliation_string":"Northwestern University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101613727"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":132.076,"has_fulltext":true,"cited_by_count":1929,"citation_normalized_percentile":{"value":1.0,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"33","issue":"01","first_page":"7370","last_page":"7377"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.998199999332428,"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":0.998199999332428,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9955999851226807,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9932000041007996,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7750920057296753},{"id":"https://openalex.org/keywords/text-graph","display_name":"Text graph","score":0.6622608304023743},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6484872102737427},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5947151780128479},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5612486004829407},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5133887529373169},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34384289383888245},{"id":"https://openalex.org/keywords/text-mining","display_name":"Text mining","score":0.28649216890335083},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2304023802280426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7750920057296753},{"id":"https://openalex.org/C66945725","wikidata":"https://www.wikidata.org/wiki/Q18388823","display_name":"Text graph","level":3,"score":0.6622608304023743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6484872102737427},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5947151780128479},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5612486004829407},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5133887529373169},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34384289383888245},{"id":"https://openalex.org/C71472368","wikidata":"https://www.wikidata.org/wiki/Q676880","display_name":"Text mining","level":2,"score":0.28649216890335083},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2304023802280426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v33i01.33017370","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33017370","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4725/4603","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v33i01.33017370","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33017370","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4725/4603","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.75,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G6218964800","display_name":null,"funder_award_id":"R21LM012618","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320322082","display_name":"Korea Institute of Public Finance","ror":"https://ror.org/03tgywz49"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2962946486.pdf","grobid_xml":"https://content.openalex.org/works/W2962946486.grobid-xml"},"referenced_works_count":61,"referenced_works":["https://openalex.org/W49342364","https://openalex.org/W637153065","https://openalex.org/W1522301498","https://openalex.org/W1662382123","https://openalex.org/W1826790618","https://openalex.org/W1832693441","https://openalex.org/W2104246439","https://openalex.org/W2124996938","https://openalex.org/W2131744502","https://openalex.org/W2145658888","https://openalex.org/W2153579005","https://openalex.org/W2154359981","https://openalex.org/W2170240176","https://openalex.org/W2173884055","https://openalex.org/W2187089797","https://openalex.org/W2250539671","https://openalex.org/W2250662230","https://openalex.org/W2252979829","https://openalex.org/W2407776548","https://openalex.org/W2468907370","https://openalex.org/W2470673105","https://openalex.org/W2519887557","https://openalex.org/W2562607067","https://openalex.org/W2566209712","https://openalex.org/W2585540825","https://openalex.org/W2600702321","https://openalex.org/W2612881151","https://openalex.org/W2624431344","https://openalex.org/W2726375170","https://openalex.org/W2759045585","https://openalex.org/W2784814091","https://openalex.org/W2786915849","https://openalex.org/W2788667846","https://openalex.org/W2799027221","https://openalex.org/W2803763037","https://openalex.org/W2805516822","https://openalex.org/W2808129629","https://openalex.org/W2912503608","https://openalex.org/W2952186591","https://openalex.org/W2963224980","https://openalex.org/W2963355447","https://openalex.org/W2963497309","https://openalex.org/W2963626623","https://openalex.org/W2963653811","https://openalex.org/W2963695795","https://openalex.org/W2963912736","https://openalex.org/W2964015378","https://openalex.org/W2964046515","https://openalex.org/W2964051675","https://openalex.org/W2964301648","https://openalex.org/W2964321699","https://openalex.org/W4294170691","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W6631190155","https://openalex.org/W6638727336","https://openalex.org/W6666761814","https://openalex.org/W6691431627","https://openalex.org/W6719819555","https://openalex.org/W6750467700","https://openalex.org/W6947939616"],"related_works":["https://openalex.org/W3181746755","https://openalex.org/W2521062615","https://openalex.org/W3016958897","https://openalex.org/W4283379348","https://openalex.org/W4312417841","https://openalex.org/W2735477435","https://openalex.org/W3045739591","https://openalex.org/W2807436399","https://openalex.org/W2767651786","https://openalex.org/W2912288872"],"abstract_inverted_index":{"Text":[0,90,100,142,161,179,200],"classification":[1],"is":[2,102],"an":[3],"important":[4],"and":[5,83,109,120,167],"classical":[6],"problem":[7],"in":[8,206],"natural":[9],"language":[10],"processing.":[11],"There":[12],"have":[13,39],"been":[14],"a":[15,34,72,77,89,140],"number":[16,36],"of":[17,37,178,193,199],"studies":[18,38],"that":[19,139,175],"applied":[20],"convolutional":[21,45,65],"neural":[22,46],"networks":[23,47,66],"(convolution":[24,48],"on":[25,49,80,134],"regular":[26],"grid,":[27],"e.g.,":[28,51],"sequence)":[29],"to":[30,62,202],"classification.":[31,69,156,208],"However,":[32],"only":[33],"limited":[35],"explored":[40],"the":[41,55,97,115,125,158,176,191,197],"more":[42,186],"flexible":[43],"graph":[44,64,75],"non-grid,":[50],"arbitrary":[52],"graph)":[53],"for":[54,67,76,96,107,117,129,154],"task.":[56],"In":[57,170],"this":[58],"work,":[59],"we":[60,189],"propose":[61],"use":[63],"text":[68,74,155,207],"We":[70],"build":[71],"single":[73],"corpus":[78],"based":[79],"word":[81,85,108,147,166],"co-occurrence":[82],"document":[84,168],"relations,":[86],"then":[87,112],"learn":[88],"Graph":[91],"Convolutional":[92],"Network":[93],"(Text":[94],"GCN)":[95],"corpus.":[98],"Our":[99,131],"GCN":[101,143,162,180,201],"initialized":[103],"with":[104],"one-hot":[105],"representation":[106],"document,":[110],"it":[111],"jointly":[113],"learns":[114,164],"embeddings":[116,148],"both":[118],"words":[119],"documents,":[121],"as":[122,188],"supervised":[123],"by":[124],"known":[126],"class":[127],"labels":[128],"documents.":[130],"experimental":[132,172],"results":[133,173],"multiple":[135],"benchmark":[136],"datasets":[137],"demonstrate":[138],"vanilla":[141],"without":[144],"any":[145],"external":[146],"or":[149],"knowledge":[150],"outperforms":[151],"state-of-the-art":[152,182],"methods":[153,184],"On":[157],"other":[159],"hand,":[160],"also":[163],"predictive":[165],"embeddings.":[169],"addition,":[171],"show":[174],"improvement":[177],"over":[181],"comparison":[183],"become":[185],"prominent":[187],"lower":[190],"percentage":[192],"training":[194,204],"data,":[195],"suggesting":[196],"robustness":[198],"less":[203],"data":[205]},"counts_by_year":[{"year":2026,"cited_by_count":32},{"year":2025,"cited_by_count":228},{"year":2024,"cited_by_count":307},{"year":2023,"cited_by_count":368},{"year":2022,"cited_by_count":357},{"year":2021,"cited_by_count":366},{"year":2020,"cited_by_count":232},{"year":2019,"cited_by_count":37},{"year":2018,"cited_by_count":2}],"updated_date":"2026-04-12T07:58:50.170612","created_date":"2025-10-10T00:00:00"}
