{"id":"https://openalex.org/W7160437338","doi":"https://doi.org/10.48550/arxiv.2605.03076","title":"Adaptive Negative Scheduling for Graph Contrastive Learning","display_name":"Adaptive Negative Scheduling for Graph Contrastive Learning","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160437338","doi":"https://doi.org/10.48550/arxiv.2605.03076"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03076","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03076","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.03076","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135442466","display_name":"Adnan Ali","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ali, Adnan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135439773","display_name":"Jinlong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jinlong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058405738","display_name":"Syed Muhammad Israr","orcid":"https://orcid.org/0000-0002-8306-9905"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Israr, Syed Muhammad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088617561","display_name":"Ali Kashif Bashir","orcid":"https://orcid.org/0000-0003-2601-9327"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bashir, Ali Kashif","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9380000233650208,"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":0.9380000233650208,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.016499999910593033,"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":0.010300000198185444,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5123999714851379},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4765999913215637},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4677000045776367},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.374099999666214},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.3028999865055084},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.2732999920845032}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7574999928474426},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5123999714851379},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5087000131607056},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4765999913215637},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44609999656677246},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.385699987411499},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.374099999666214},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C139502532","wikidata":"https://www.wikidata.org/wiki/Q1122090","display_name":"Computational intelligence","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03076","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03076","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.03076","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03076","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Graph":[0],"contrastive":[1,88],"learning":[2,12,162],"(GCL)":[3],"has":[4],"become":[5],"a":[6,61,70,151],"central":[7],"paradigm":[8],"for":[9,41,154],"self-supervised":[10],"representation":[11,161],"in":[13,163],"computational":[14,46,139,165],"intelligence,":[15],"with":[16,60],"applications":[17],"spanning":[18],"recommendation,":[19],"anomaly":[20],"detection,":[21],"and":[22,45,77,94,128,158],"personalization.":[23],"A":[24],"key":[25],"limitation":[26],"of":[27,48,146,160],"existing":[28],"methods":[29],"is":[30],"their":[31],"reliance":[32],"on":[33,87,109,125,130],"static":[34],"negative":[35,57,67],"sampling,":[36],"which":[37],"fails":[38],"to":[39,101],"account":[40],"the":[42,122,131,144,156],"dynamic":[43],"informativeness":[44],"cost":[47],"negatives":[49],"during":[50],"training.":[51],"We":[52],"propose":[53],"AdNGCL,":[54],"an":[55],"adaptive":[56],"scheduling":[58,149],"framework":[59],"hardness-aware":[62],"scheduler":[63,81],"(HANS)":[64],"that":[65,115],"formulates":[66],"selection":[68],"as":[69,150],"loss-gated,":[71],"budget-constrained":[72],"process":[73],"across":[74],"hard,":[75],"intermediate,":[76],"easy":[78],"strata.":[79],"The":[80],"dynamically":[82],"adjusts":[83],"step":[84],"sizes":[85],"based":[86],"loss":[89],"trends":[90],"under":[91],"both":[92],"global":[93],"per-category":[95],"budgets,":[96],"while":[97,134],"periodically":[98],"refreshing":[99],"samples":[100],"maintain":[102],"diversity":[103],"without":[104],"exceeding":[105],"compute":[106],"constraints.":[107],"Experiments":[108],"nine":[110],"benchmark":[111],"graph":[112],"datasets":[113,127],"demonstrate":[114],"AdNGCL":[116],"consistently":[117],"advances":[118],"state-of-the-art":[119],"performance,":[120],"achieving":[121],"best":[123],"accuracy":[124],"seven":[126],"second-best":[129],"remaining":[132],"two,":[133],"offering":[135],"explicit":[136],"control":[137],"over":[138],"cost.":[140],"These":[141],"results":[142],"highlight":[143],"value":[145],"budget-aware,":[147],"loss-sensitive":[148],"general":[152],"strategy":[153],"improving":[155],"robustness":[157],"efficiency":[159],"emerging":[164],"intelligence":[166],"applications.":[167]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-07T00:00:00"}