{"id":"https://openalex.org/W3080456792","doi":"https://doi.org/10.1145/3394486.3403218","title":"Understanding Negative Sampling in Graph Representation Learning","display_name":"Understanding Negative Sampling in Graph Representation Learning","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W3080456792","doi":"https://doi.org/10.1145/3394486.3403218","mag":"3080456792"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403218","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394486.3403218","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"article","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/A5115883486","display_name":"Zhen Yang","orcid":"https://orcid.org/0000-0003-2883-7665"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhen Yang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100606824","display_name":"Ming Ding","orcid":"https://orcid.org/0000-0002-4919-5772"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Ding","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102025315","display_name":"Chang Zhou","orcid":"https://orcid.org/0000-0001-9241-702X"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang Zhou","raw_affiliation_strings":["DAMO Academy, Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"DAMO Academy, Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082599714","display_name":"Hongxia Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongxia Yang","raw_affiliation_strings":["DAMO Academy, Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"DAMO Academy, Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057864403","display_name":"Jingren Zhou","orcid":"https://orcid.org/0000-0002-4220-2634"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingren Zhou","raw_affiliation_strings":["DAMO Academy, Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"DAMO Academy, Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044791875","display_name":"Jie Tang","orcid":"https://orcid.org/0000-0003-3487-4593"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Tang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5115883486"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":12.9125,"has_fulltext":false,"cited_by_count":169,"citation_normalized_percentile":{"value":0.98982867,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1666","last_page":"1676"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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/T10028","display_name":"Topic Modeling","score":0.9807000160217285,"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":0.9789000153541565,"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":0.6459037661552429},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6089892387390137},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.557863175868988},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5358641743659973},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5325334072113037},{"id":"https://openalex.org/keywords/experience-sampling-method","display_name":"Experience sampling method","score":0.44989341497421265},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3866826891899109},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.38646402955055237},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36921268701553345},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.315094530582428},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.290446400642395}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6459037661552429},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6089892387390137},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.557863175868988},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5358641743659973},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5325334072113037},{"id":"https://openalex.org/C65499552","wikidata":"https://www.wikidata.org/wiki/Q5421061","display_name":"Experience sampling method","level":2,"score":0.44989341497421265},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3866826891899109},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38646402955055237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36921268701553345},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.315094530582428},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.290446400642395},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3394486.3403218","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394486.3403218","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5439037276","display_name":null,"funder_award_id":"61825602","funder_id":"https://openalex.org/F4320327720","funder_display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320327720","display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W82211948","https://openalex.org/W1888005072","https://openalex.org/W2027731328","https://openalex.org/W2062340319","https://openalex.org/W2090679992","https://openalex.org/W2101409192","https://openalex.org/W2102035799","https://openalex.org/W2108614537","https://openalex.org/W2124187902","https://openalex.org/W2138204974","https://openalex.org/W2150886314","https://openalex.org/W2152808281","https://openalex.org/W2154851992","https://openalex.org/W2619206542","https://openalex.org/W2740934577","https://openalex.org/W2798746280","https://openalex.org/W2799079108","https://openalex.org/W2807021761","https://openalex.org/W2809001617","https://openalex.org/W2809291355","https://openalex.org/W2891649471","https://openalex.org/W2905267911","https://openalex.org/W2962756421","https://openalex.org/W2963642516","https://openalex.org/W2964051675","https://openalex.org/W3001645704","https://openalex.org/W3100848837","https://openalex.org/W3101023724","https://openalex.org/W3103995645","https://openalex.org/W3104038788","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W3171249018","https://openalex.org/W4247880210","https://openalex.org/W4291474301"],"related_works":["https://openalex.org/W2039864646","https://openalex.org/W2809023326","https://openalex.org/W4298005780","https://openalex.org/W4310208846","https://openalex.org/W2770593030","https://openalex.org/W2315273608","https://openalex.org/W1996287031","https://openalex.org/W2330004501","https://openalex.org/W2017089693","https://openalex.org/W2703295919"],"abstract_inverted_index":{"Graph":[0],"representation":[1],"learning":[2,141],"has":[3],"been":[4],"extensively":[5],"studied":[6],"in":[7,10,67],"recent":[8],"years,":[9],"which":[11],"sampling":[12,22,31,47,60,66,96,126],"is":[13,32,61,98],"a":[14,93,151],"critical":[15],"point.":[16],"Prior":[17],"arts":[18],"usually":[19],"focus":[20],"on":[21,133,150],"positive":[23,65,118],"node":[24,146],"pairs,":[25],"while":[26],"the":[27,38,43,49,69,73,77,84,88,108,111,117],"strategy":[28],"for":[29],"negative":[30,46,59,95,125],"left":[33],"insufficiently":[34],"explored.":[35],"To":[36,76],"bridge":[37],"gap,":[39],"we":[40,82,113],"systematically":[41],"analyze":[42],"role":[44],"of":[45,51,79,110,153],"from":[48],"perspectives":[50],"both":[52],"objective":[53,71],"and":[54,72,90,123,148,165],"risk,":[55],"theoretically":[56],"demonstrating":[57],"that":[58,92,136],"as":[62,64],"important":[63],"determining":[68],"optimization":[70],"resulted":[74],"variance.":[75],"best":[78],"our":[80,131],"knowledge,":[81],"are":[83],"first":[85],"to":[86],"derive":[87],"theory":[89],"quantify":[91],"nice":[94],"distribution":[97,119],"pn(u|v)":[99],"\u221d":[100],"pd(u|v)\u03b1,":[101],"0":[102],"<":[103,105],"\u03b1":[104],"1.":[106],"With":[107],"guidance":[109],"theory,":[112],"propose":[114],"MCNS,":[115],"approximating":[116],"with":[120],"self-contrast":[121],"approximation":[122],"accelerating":[124],"by":[127],"Metropolis-Hastings.":[128],"We":[129],"evaluate":[130],"method":[132],"5":[134],"datasets":[135],"cover":[137],"extensive":[138],"downstream":[139],"graph":[140],"tasks,":[142],"including":[143],"link":[144],"prediction,":[145],"classification":[147],"recommendation,":[149],"total":[152],"19":[154],"experimental":[155,160],"settings.":[156],"These":[157],"relatively":[158],"comprehensive":[159],"results":[161],"demonstrate":[162],"its":[163],"robustness":[164],"superiorities.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":29},{"year":2024,"cited_by_count":39},{"year":2023,"cited_by_count":35},{"year":2022,"cited_by_count":36},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":6}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}
