{"id":"https://openalex.org/W2983088090","doi":"https://doi.org/10.1145/3357384.3357903","title":"Multi-Hot Compact Network Embedding","display_name":"Multi-Hot Compact Network Embedding","publication_year":2019,"publication_date":"2019-11-03","ids":{"openalex":"https://openalex.org/W2983088090","doi":"https://doi.org/10.1145/3357384.3357903","mag":"2983088090"},"language":"en","primary_location":{"id":"doi:10.1145/3357384.3357903","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037831162","display_name":"Chaozhuo Li","orcid":"https://orcid.org/0000-0002-9867-1712"},"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":"Chaozhuo Li","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101698631","display_name":"Lei Zheng","orcid":"https://orcid.org/0000-0002-9043-2506"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Zheng","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035708362","display_name":"Senzhang Wang","orcid":"https://orcid.org/0000-0002-3615-4859"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Senzhang Wang","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033233243","display_name":"Feiran Huang","orcid":"https://orcid.org/0000-0003-4294-0212"},"institutions":[{"id":"https://openalex.org/I159948400","display_name":"Jinan University","ror":"https://ror.org/02xe5ns62","country_code":"CN","type":"education","lineage":["https://openalex.org/I159948400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiran Huang","raw_affiliation_strings":["Jinan University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jinan University, Guangzhou, China","institution_ids":["https://openalex.org/I159948400"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036357902","display_name":"Philip S. Yu","orcid":"https://orcid.org/0000-0002-3491-5968"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip S. Yu","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036786337","display_name":"Zhoujun Li","orcid":"https://orcid.org/0000-0002-9603-9713"},"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":"Zhoujun Li","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7654,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.78335686,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"459","last_page":"468"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9854999780654907,"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":0.9819999933242798,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7665421962738037},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.753110408782959},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6881195306777954},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6530264019966125},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.54563307762146},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.46281754970550537},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.45253050327301025},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.36818891763687134},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3445373475551605},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3220052719116211},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12168377637863159}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7665421962738037},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.753110408782959},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6881195306777954},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6530264019966125},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.54563307762146},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.46281754970550537},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45253050327301025},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.36818891763687134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3445373475551605},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3220052719116211},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12168377637863159},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3357384.3357903","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1576278180","https://openalex.org/W1888005072","https://openalex.org/W2041836310","https://openalex.org/W2090891622","https://openalex.org/W2114424556","https://openalex.org/W2154851992","https://openalex.org/W2242161203","https://openalex.org/W2325846041","https://openalex.org/W2393319904","https://openalex.org/W2579372251","https://openalex.org/W2601615955","https://openalex.org/W2782733358","https://openalex.org/W2798932700","https://openalex.org/W2808000122","https://openalex.org/W2808373708","https://openalex.org/W2809219720","https://openalex.org/W2809660921","https://openalex.org/W2962756421","https://openalex.org/W3104097132","https://openalex.org/W3105705953"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2183306018","https://openalex.org/W2549990292","https://openalex.org/W2345479200","https://openalex.org/W2951819827","https://openalex.org/W2849310602","https://openalex.org/W2419146053","https://openalex.org/W2088247287","https://openalex.org/W1588041347","https://openalex.org/W2781863720"],"abstract_inverted_index":{"Network":[0],"embedding,":[1],"as":[2],"a":[3,52,73,85,115],"promising":[4],"way":[5],"of":[6,13,79,103,190,193],"the":[7,101,139,164,171,180],"network":[8,17,56,166,177,194],"representation":[9,110],"learning,":[10],"is":[11,71,77,132],"capable":[12],"supporting":[14],"various":[15],"subsequent":[16],"mining":[18],"and":[19,22,179],"analysis":[20],"tasks,":[21],"has":[23],"attracted":[24],"growing":[25],"research":[26],"interests":[27],"recently.":[28],"Traditional":[29],"approaches":[30],"assign":[31],"each":[32],"node":[33,74,125],"with":[34],"an":[35,154],"independent":[36],"continuous":[37,104],"vector,":[38],"which":[39,97],"will":[40],"cause":[41],"memory":[42,62,191],"overhead":[43],"for":[44],"large":[45],"networks.":[46],"In":[47],"this":[48],"paper":[49],"we":[50,113,169],"propose":[51,114,153],"novel":[53,128],"multi-hot":[54,86],"compact":[55,121,161],"embedding":[57,75],"framework":[58],"to":[59,84,119,137,159],"effectively":[60],"reduce":[61,100],"cost":[63,192],"by":[64,94],"learning":[65],"partially":[66],"shared":[67,93],"embeddings.":[68],"The":[69,89],"insight":[70],"that":[72,141,183],"vector":[76],"composed":[78],"several":[80],"basis":[81,90],"vectors":[82,91,105],"according":[83],"index":[87],"vector.":[88],"are":[92],"different":[95],"nodes,":[96],"can":[98,186],"significantly":[99,197],"number":[102],"while":[106],"maintain":[107],"similar":[108],"data":[109],"ability.":[111],"Specifically,":[112],"MCNE$_p":[116,135],"$":[117,136,158],"model":[118,156],"learn":[120,160],"embeddings":[122,162,195],"from":[123,163],"pre-learned":[124],"features.":[126],"A":[127],"component":[129],"named":[130],"compressor":[131],"integrated":[133],"into":[134],"tackle":[138],"challenge":[140],"popular":[142],"back-propagation":[143],"optimization":[144],"cannot":[145],"propagate":[146],"loss":[147],"through":[148],"discrete":[149],"samples.":[150],"We":[151],"further":[152],"end-to-end":[155],"MCNE$_t":[157],"input":[165],"directly.":[167],"Empirically,":[168],"evaluate":[170],"proposed":[172],"models":[173],"over":[174],"four":[175],"real":[176],"datasets,":[178],"results":[181],"demonstrate":[182],"our":[184],"proposals":[185],"save":[187],"about":[188],"90%":[189],"without":[196],"performance":[198],"decline.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}