{"id":"https://openalex.org/W2997932753","doi":"https://doi.org/10.1145/3336191.3371806","title":"Deep Multi-Graph Clustering via Attentive Cross-Graph Association","display_name":"Deep Multi-Graph Clustering via Attentive Cross-Graph Association","publication_year":2020,"publication_date":"2020-01-20","ids":{"openalex":"https://openalex.org/W2997932753","doi":"https://doi.org/10.1145/3336191.3371806","mag":"2997932753"},"language":"en","primary_location":{"id":"doi:10.1145/3336191.3371806","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3336191.3371806","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3336191.3371806","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3336191.3371806","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102761357","display_name":"Dongsheng Luo","orcid":"https://orcid.org/0000-0003-4192-0826"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dongsheng Luo","raw_affiliation_strings":["Pennsylvania State University, State College, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, State College, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043236347","display_name":"Jingchao Ni","orcid":"https://orcid.org/0000-0002-2986-6612"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingchao Ni","raw_affiliation_strings":["NEC Laboratories America, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories America, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011048500","display_name":"Suhang Wang","orcid":"https://orcid.org/0000-0003-3448-4878"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suhang Wang","raw_affiliation_strings":["Pennsylvania State University, State College, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, State College, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005150470","display_name":"Yuchen Bian","orcid":"https://orcid.org/0000-0002-0685-3771"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuchen Bian","raw_affiliation_strings":["Baidu Research, Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"Baidu Research, Sunnyvale, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047074008","display_name":"Xiong Yu","orcid":"https://orcid.org/0000-0001-6879-2567"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiong Yu","raw_affiliation_strings":["Case Western Reserve University, Cleveland, OH, USA"],"affiliations":[{"raw_affiliation_string":"Case Western Reserve University, Cleveland, OH, USA","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033796010","display_name":"Xiang Zhang","orcid":"https://orcid.org/0000-0002-7432-9904"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Zhang","raw_affiliation_strings":["Pennsylvania State University, State College, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, State College, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5102761357"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":2.7188,"has_fulltext":true,"cited_by_count":27,"citation_normalized_percentile":{"value":0.91957021,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"393","last_page":"401"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9987000226974487,"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":0.982200026512146,"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":0.7598153352737427},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7164747714996338},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5457801222801208},{"id":"https://openalex.org/keywords/clustering-coefficient","display_name":"Clustering coefficient","score":0.5402756333351135},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5147071480751038},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4496515095233917},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.44870084524154663},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4207385778427124},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3745219111442566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3673221468925476}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7598153352737427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7164747714996338},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5457801222801208},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.5402756333351135},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5147071480751038},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4496515095233917},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.44870084524154663},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4207385778427124},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3745219111442566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3673221468925476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3336191.3371806","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3336191.3371806","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3336191.3371806","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3336191.3371806","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3336191.3371806","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3336191.3371806","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G594296252","display_name":null,"funder_award_id":"IIS-1707548, CBET-1638320","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7405368409","display_name":"CAREER: Novel Approaches for Mining Large and Complex Networks","funder_award_id":"1707548","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G762323281","display_name":null,"funder_award_id":"IIS-1707548","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337390","display_name":"Division of Chemical, Bioengineering, Environmental, and Transport Systems","ror":"https://ror.org/0471zv972"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2997932753.pdf","grobid_xml":"https://content.openalex.org/works/W2997932753.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1504886279","https://openalex.org/W1531883353","https://openalex.org/W1532325895","https://openalex.org/W1533861849","https://openalex.org/W1995875735","https://openalex.org/W2015953751","https://openalex.org/W2019247858","https://openalex.org/W2022322548","https://openalex.org/W2024729467","https://openalex.org/W2030724586","https://openalex.org/W2113573459","https://openalex.org/W2132914434","https://openalex.org/W2154851992","https://openalex.org/W2161494102","https://openalex.org/W2162941511","https://openalex.org/W2187089797","https://openalex.org/W2240268387","https://openalex.org/W2341234495","https://openalex.org/W2393319904","https://openalex.org/W2405933695","https://openalex.org/W2604942799","https://openalex.org/W2624431344","https://openalex.org/W2738452247","https://openalex.org/W2767849480","https://openalex.org/W2775217026","https://openalex.org/W2788816357","https://openalex.org/W2801116648","https://openalex.org/W2901402683","https://openalex.org/W2952991498","https://openalex.org/W2962756421","https://openalex.org/W2964074409","https://openalex.org/W2976859544","https://openalex.org/W3099511899","https://openalex.org/W3104097132","https://openalex.org/W6631834165"],"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":{"Multi-graph":[0],"clustering":[1,5,24,30,38,110],"aims":[2],"to":[3,17,47,85,100,112,126,139],"improve":[4],"accuracy":[6],"by":[7,66,160],"leveraging":[8],"information":[9],"from":[10],"different":[11],"domains,":[12],"which":[13,44],"has":[14],"been":[15],"shown":[16],"be":[18],"extremely":[19],"effective":[20],"for":[21,119],"achieving":[22],"better":[23],"results":[25],"than":[26],"single":[27],"graph":[28],"based":[29,109],"algorithms.":[31],"Despite":[32],"the":[33,49,54,67,116],"previous":[34],"success,":[35],"existing":[36],"multi-graph":[37],"methods":[39],"mostly":[40],"use":[41,97],"shallow":[42],"models,":[43],"are":[45],"incapable":[46],"capture":[48],"highly":[50],"non-linear":[51],"structures":[52],"and":[53,90,130],"complex":[55],"cluster":[56,88,91,113,142],"associations":[57,92],"in":[58,62,75,93,115],"multi-graph,":[59],"thus":[60],"result":[61],"sub-optimal":[63],"results.":[64],"Inspired":[65],"powerful":[68],"representation":[69],"learning":[70,83],"capability":[71],"of":[72,150],"neural":[73],"networks,":[74],"this":[76],"paper,":[77],"we":[78,96,105,152],"propose":[79,106],"an":[80],"end-to-end":[81],"deep":[82],"model":[84],"simultaneously":[86],"infer":[87],"assignments":[89],"multi-graph.":[94],"Specifically,":[95],"autoencoding":[98],"networks":[99],"learn":[101,140],"node":[102],"embeddings.":[103],"Meanwhile,":[104],"a":[107,148,161],"minimum-entropy":[108],"strategy":[111],"nodes":[114],"embedding":[117],"space":[118],"each":[120],"graph.":[121],"We":[122],"introduce":[123],"two":[124],"regularizers":[125],"leverage":[127],"both":[128],"within-graph":[129],"cross-graph":[131,141],"dependencies.":[132],"An":[133],"attentive":[134],"mechanism":[135],"is":[136],"further":[137],"developed":[138],"associations.":[143],"Through":[144],"extensive":[145],"experiments":[146],"on":[147],"variety":[149],"datasets,":[151],"observe":[153],"that":[154],"our":[155],"method":[156],"outperforms":[157],"state-of-the-art":[158],"baselines":[159],"large":[162],"margin.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}