{"id":"https://openalex.org/W4290875392","doi":"https://doi.org/10.1145/3534678.3539324","title":"Rep2Vec: Repository Embedding via Heterogeneous Graph Adversarial Contrastive Learning","display_name":"Rep2Vec: Repository Embedding via Heterogeneous Graph Adversarial Contrastive Learning","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290875392","doi":"https://doi.org/10.1145/3534678.3539324"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539324","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539324","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and 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/A5064649082","display_name":"Yiyue Qian","orcid":"https://orcid.org/0000-0003-4288-7941"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yiyue Qian","raw_affiliation_strings":["University of Notre Dame, South Bend, IN, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, South Bend, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100395392","display_name":"Yiming Zhang","orcid":"https://orcid.org/0009-0001-0233-3541"},"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":"Yiming Zhang","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":"middle","author":{"id":"https://openalex.org/A5006218899","display_name":"Qianlong Wen","orcid":"https://orcid.org/0000-0003-3812-8395"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qianlong Wen","raw_affiliation_strings":["University of Notre Dame, South Bend, IN, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, South Bend, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027601906","display_name":"Yanfang Ye","orcid":"https://orcid.org/0000-0002-6038-2173"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanfang Ye","raw_affiliation_strings":["University of Notre Dame, South Bend, IN, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, South Bend, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022275632","display_name":"Chuxu Zhang","orcid":"https://orcid.org/0000-0002-8349-7926"},"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":"Chuxu Zhang","raw_affiliation_strings":["Brandeis University, Waltham, MA, USA"],"affiliations":[{"raw_affiliation_string":"Brandeis University, Waltham, MA, USA","institution_ids":["https://openalex.org/I6902469"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5064649082"],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":1.7642,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.86953243,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1390","last_page":"1400"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9764000177383423,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9764000177383423,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9625999927520752,"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/T10166","display_name":"Mosquito-borne diseases and control","score":0.9502000212669373,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8544397354125977},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7365105152130127},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.5993243455886841},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.5042232275009155},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4603204131126404},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4569985270500183},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4499666094779968},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4356961250305176},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4325801730155945},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.40042099356651306},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3726657032966614},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.20886439085006714},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.10082954168319702}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8544397354125977},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7365105152130127},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.5993243455886841},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.5042232275009155},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4603204131126404},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4569985270500183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4499666094779968},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4356961250305176},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4325801730155945},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40042099356651306},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3726657032966614},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.20886439085006714},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.10082954168319702},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539324","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539324","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6200000047683716}],"awards":[{"id":"https://openalex.org/G2903890082","display_name":null,"funder_award_id":"IIS-2209814,IIS-2203262,IIS-2214376,CNS-2203261,CNS-1814825,IIS-2217239,IIS-2027127,OAC-2218762","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3468583100","display_name":null,"funder_award_id":"2018-75-CX-0032","funder_id":"https://openalex.org/F4320337430","funder_display_name":"National Institute of Justice"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337430","display_name":"National Institute of Justice","ror":"https://ror.org/00v8p7w89"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W2154851992","https://openalex.org/W2743104969","https://openalex.org/W2744097819","https://openalex.org/W2796225030","https://openalex.org/W2803831897","https://openalex.org/W2891798678","https://openalex.org/W2907253296","https://openalex.org/W2911286998","https://openalex.org/W2962756421","https://openalex.org/W2965857891","https://openalex.org/W2984482968","https://openalex.org/W3015474461","https://openalex.org/W3036446966","https://openalex.org/W3084489712","https://openalex.org/W3098276446","https://openalex.org/W3099152386","https://openalex.org/W3100491867","https://openalex.org/W3104097132","https://openalex.org/W3112920084","https://openalex.org/W3172710079","https://openalex.org/W3177721927","https://openalex.org/W3190141738","https://openalex.org/W3200170138","https://openalex.org/W4226057794"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2502115930","https://openalex.org/W4386541577"],"abstract_inverted_index":{"Driven":[0],"by":[1,171,251],"the":[2,8,11,31,98,123,139,142,146,151,158,195,201,212,215,239,245,259,265],"exponential":[3],"increase":[4],"of":[5,10,19,59,74,89,111,214,267],"software":[6,21,43],"and":[7,38,44,145,206,234,248],"advent":[9],"pull-based":[12],"development":[13],"system":[14],"Git,":[15],"a":[16,52,109,133,163,172,222,252],"large":[17],"amount":[18],"open-source":[20],"has":[22,49],"emerged":[23],"on":[24,76,93,231,258],"various":[25,79],"social":[26],"coding":[27],"platforms.":[28],"GitHub,":[29],"as":[30],"largest":[32],"platform,":[33],"not":[34],"only":[35],"attracts":[36],"developers":[37],"researchers":[39],"to":[40,61,71,113,149,155,177,188,199,244],"contribute":[41],"legitimate":[42],"research-related":[45],"source":[46],"code":[47,94,140],"but":[48],"also":[50],"become":[51],"popular":[53],"platform":[54],"for":[55,78,118,194,274],"an":[56],"increasing":[57],"number":[58],"cybercriminals":[60],"perform":[62],"continuous":[63],"cyberattacks.":[64],"Hence,":[65],"some":[66],"tools":[67],"have":[68],"been":[69],"developed":[70],"learn":[72,150],"representations":[73],"repositories":[75],"GitHub":[77,263],"related":[80],"applications":[81],"(e.g.,":[82],"malicious":[83],"repository":[84,152,159,164,276],"detection)":[85],"recently.":[86],"However,":[87],"most":[88],"them":[90],"merely":[91],"focus":[92],"content":[95],"while":[96,121],"ignoring":[97,122],"rich":[99],"relational":[100],"data":[101,117,148,181],"among":[102],"repositories.":[103],"In":[104],"addition,":[105],"they":[106],"usually":[107],"require":[108],"mass":[110],"resources":[112],"obtain":[114],"sufficient":[115],"labeled":[116],"model":[119,135,157],"training":[120],"usefully":[124],"handy":[125],"unlabeled":[126,147,180],"data.":[127],"To":[128,210],"this":[129],"end,":[130],"we":[131,161,184,219],"propose":[132],"novel":[134],"Rep2Vec":[136,268],"which":[137,168],"integrates":[138,228],"content,":[141],"structural":[143],"relations,":[144],"representations.":[153],"First,":[154],"comprehensively":[156],"data,":[160],"build":[162],"heterogeneous":[165],"graph":[166,173,233,236],"(Rep-HG)":[167],"is":[169,242],"encoded":[170],"neural":[174],"network.":[175],"Afterwards,":[176],"fully":[178],"exploit":[179],"in":[182,203,269],"Rep-HG,":[183],"introduce":[185],"adversarial":[186,232],"attacks":[187],"generate":[189],"more":[190],"challenging":[191],"contrastive":[192,196,224,229],"pairs":[193],"learning":[197,225,230],"module":[198,226],"train":[200],"encoder":[202,216,241],"node":[204],"view":[205,208],"meta-path":[207],"simultaneously.":[209],"alleviate":[211],"workload":[213],"against":[217],"attacks,":[218],"further":[220,249],"design":[221],"dual-stream":[223],"that":[227],"original":[235],"together.":[237],"Finally,":[238],"pre-trained":[240],"fine-tuned":[243],"downstream":[246],"task,":[247],"enhanced":[250],"knowledge":[253],"distillation":[254],"module.":[255],"Extensive":[256],"experiments":[257],"collected":[260],"dataset":[261],"from":[262],"demonstrate":[264],"effectiveness":[266],"comparison":[270],"with":[271],"state-of-the-art":[272],"methods":[273],"multiple":[275],"tasks.":[277]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}