{"id":"https://openalex.org/W2966028836","doi":"https://doi.org/10.24963/ijcai.2019/494","title":"Adversarial Graph Embedding for Ensemble Clustering","display_name":"Adversarial Graph Embedding for Ensemble Clustering","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2966028836","doi":"https://doi.org/10.24963/ijcai.2019/494","mag":"2966028836"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/494","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/494","pdf_url":"https://www.ijcai.org/proceedings/2019/0494.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2019/0494.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021486404","display_name":"Zhiqiang Tao","orcid":"https://orcid.org/0000-0002-5639-7540"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhiqiang Tao","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Northeastern University, Boston, MA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Northeastern University, Boston, MA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101987910","display_name":"Hongfu Liu","orcid":"https://orcid.org/0000-0002-0821-8640"},"institutions":[{"id":"https://openalex.org/I6902469","display_name":"Brandeis University","ror":"https://ror.org/05abbep66","country_code":"US","type":"education","lineage":["https://openalex.org/I6902469"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongfu Liu","raw_affiliation_strings":["Michtom School of Computer Science, Brandeis University, Waltham, MA"],"affiliations":[{"raw_affiliation_string":"Michtom School of Computer Science, Brandeis University, Waltham, MA","institution_ids":["https://openalex.org/I6902469"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100361625","display_name":"Jun Li","orcid":"https://orcid.org/0000-0001-5845-8602"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Li","raw_affiliation_strings":["Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA"],"affiliations":[{"raw_affiliation_string":"Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068625652","display_name":"Zhaowen Wang","orcid":"https://orcid.org/0000-0002-4605-2506"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhaowen Wang","raw_affiliation_strings":["Adobe Research, Adobe Systems Incorporated, San Jose, CA"],"affiliations":[{"raw_affiliation_string":"Adobe Research, Adobe Systems Incorporated, San Jose, CA","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005819096","display_name":"Yun Fu","orcid":"https://orcid.org/0000-0002-5098-2853"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yun Fu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Northeastern University, Boston, MA","Khoury College of Computer and Information Sciences, Northeastern University, Boston, MA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Northeastern University, Boston, MA","institution_ids":["https://openalex.org/I12912129"]},{"raw_affiliation_string":"Khoury College of Computer and Information Sciences, Northeastern University, Boston, MA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5021486404"],"corresponding_institution_ids":["https://openalex.org/I12912129"],"apc_list":null,"apc_paid":null,"fwci":4.6264,"has_fulltext":false,"cited_by_count":78,"citation_normalized_percentile":{"value":0.95822353,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3562","last_page":"3568"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9919000267982483,"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/cluster-analysis","display_name":"Cluster analysis","score":0.6883982419967651},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6824249625205994},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5434707403182983},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5106118321418762},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.510543704032898},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.49988460540771484},{"id":"https://openalex.org/keywords/graph-partition","display_name":"Graph partition","score":0.49394017457962036},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4553471505641937},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4386807084083557},{"id":"https://openalex.org/keywords/clustering-coefficient","display_name":"Clustering coefficient","score":0.42792823910713196},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4251786470413208},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.36084067821502686},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3547728955745697}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6883982419967651},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6824249625205994},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5434707403182983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5106118321418762},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.510543704032898},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.49988460540771484},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.49394017457962036},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4553471505641937},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4386807084083557},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.42792823910713196},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4251786470413208},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36084067821502686},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3547728955745697}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2019/494","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/494","pdf_url":"https://www.ijcai.org/proceedings/2019/0494.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/494","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/494","pdf_url":"https://www.ijcai.org/proceedings/2019/0494.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3571051887","display_name":null,"funder_award_id":"W911NF-17-1-0367","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G6838734070","display_name":null,"funder_award_id":"1651902","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G8998121839","display_name":null,"funder_award_id":"911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2966028836.pdf","grobid_xml":"https://content.openalex.org/works/W2966028836.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W637153065","https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1662382123","https://openalex.org/W1686810756","https://openalex.org/W1959608418","https://openalex.org/W1990063425","https://openalex.org/W2011494630","https://openalex.org/W2083177649","https://openalex.org/W2090923791","https://openalex.org/W2099471712","https://openalex.org/W2112104211","https://openalex.org/W2139280638","https://openalex.org/W2141923507","https://openalex.org/W2144419338","https://openalex.org/W2152478904","https://openalex.org/W2165874743","https://openalex.org/W2318949783","https://openalex.org/W2386997096","https://openalex.org/W2533545350","https://openalex.org/W2604847894","https://openalex.org/W2605146283","https://openalex.org/W2742098698","https://openalex.org/W2768104274","https://openalex.org/W2808409763","https://openalex.org/W2905504056","https://openalex.org/W2962852342","https://openalex.org/W2962904108","https://openalex.org/W2963169753","https://openalex.org/W2964015378","https://openalex.org/W2964074409","https://openalex.org/W2964121744","https://openalex.org/W2964311892","https://openalex.org/W2964321699","https://openalex.org/W3099209941","https://openalex.org/W3143107425","https://openalex.org/W4285719527","https://openalex.org/W4293568373","https://openalex.org/W4320013936","https://openalex.org/W4322614756"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W3038102983","https://openalex.org/W2950907416","https://openalex.org/W1559483280","https://openalex.org/W2082479932","https://openalex.org/W2932872266"],"abstract_inverted_index":{"Ensemble":[0],"clustering":[1,53,137],"generally":[2],"integrates":[3],"basic":[4],"partitions":[5],"into":[6,54],"a":[7,11,43,55,81],"consensus":[8,29,78],"one":[9],"through":[10],"graph":[12,33,57,61,92],"partitioning":[13],"method,":[14],"which,":[15],"however,":[16],"has":[17],"two":[18],"limitations:":[19],"1)":[20],"it":[21],"neglects":[22],"to":[23,50,69,90,107,124],"reuse":[24],"original":[25],"features;":[26],"2)":[27],"obtaining":[28],"partition":[30],"with":[31,93,112],"learnable":[32],"representations":[34],"is":[35,64,86],"still":[36],"under-explored.":[37],"In":[38],"this":[39],"paper,":[40],"we":[41,102],"propose":[42],"novel":[44],"Adversarial":[45],"Graph":[46],"Auto-Encoders":[47],"(AGAE)":[48],"model":[49],"incorporate":[51],"ensemble":[52,136],"deep":[56,133],"embedding":[58,99,134],"process.":[59],"Specifically,":[60],"convolutional":[62],"network":[63,110],"adopted":[65],"as":[66,88],"probabilistic":[67],"encoder":[68],"jointly":[70],"integrate":[71],"the":[72,94,109,126],"information":[73],"from":[74],"feature":[75],"content":[76],"and":[77,80,135],"graph,":[79],"simple":[82],"inner":[83],"product":[84],"layer":[85],"used":[87],"decoder":[89],"reconstruct":[91],"encoded":[95],"latent":[96],"variables":[97],"(i.e.,":[98],"representations).":[100],"Moreover,":[101],"develop":[103],"an":[104,113],"adversarial":[105],"regularizer":[106],"guide":[108],"training":[111],"adaptive":[114],"partition-dependent":[115],"prior.":[116],"Experiments":[117],"on":[118],"eight":[119],"real-world":[120],"datasets":[121],"are":[122],"presented":[123],"show":[125],"effectiveness":[127],"of":[128],"AGAE":[129],"over":[130],"several":[131],"state-of-the-art":[132],"methods.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":6}],"updated_date":"2026-03-15T09:29:46.208133","created_date":"2025-10-10T00:00:00"}