{"id":"https://openalex.org/W7155521747","doi":"https://doi.org/10.1145/3799830.3799846","title":"Can curriculum learning overcome structural disparity in MP-GNNs?","display_name":"Can curriculum learning overcome structural disparity in MP-GNNs?","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7155521747","doi":"https://doi.org/10.1145/3799830.3799846"},"language":null,"primary_location":{"id":"doi:10.1145/3799830.3799846","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799846","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3799830.3799846","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134494290","display_name":"Ushmita Pareek","orcid":"https://orcid.org/0009-0006-2739-6019"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ushmita Pareek","raw_affiliation_strings":["Mastercard AI Garage, Gurugram, India"],"raw_orcid":"https://orcid.org/0009-0006-2739-6019","affiliations":[{"raw_affiliation_string":"Mastercard AI Garage, Gurugram, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134559949","display_name":"Raunak Pandey","orcid":"https://orcid.org/0009-0005-7959-6605"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raunak Pandey","raw_affiliation_strings":["Mastercard AI Garage, Gurugram, India"],"raw_orcid":"https://orcid.org/0009-0005-7959-6605","affiliations":[{"raw_affiliation_string":"Mastercard AI Garage, Gurugram, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134486542","display_name":"Krisha Shah","orcid":"https://orcid.org/0009-0003-4219-0527"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krisha Shah","raw_affiliation_strings":["Mastercard AI Garage, Gurugram, India"],"raw_orcid":"https://orcid.org/0009-0003-4219-0527","affiliations":[{"raw_affiliation_string":"Mastercard AI Garage, Gurugram, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120443522","display_name":"B Srinath Achary","orcid":"https://orcid.org/0009-0006-5656-2899"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"B Srinath Achary","raw_affiliation_strings":["Mastercard AI Garage, Gurugram, India"],"raw_orcid":"https://orcid.org/0009-0006-5656-2899","affiliations":[{"raw_affiliation_string":"Mastercard AI Garage, Gurugram, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054341033","display_name":"Sonia Gupta","orcid":"https://orcid.org/0000-0001-6143-8653"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sonia Gupta","raw_affiliation_strings":["Mastercard AI Garage, Gurugram, India"],"raw_orcid":"https://orcid.org/0000-0001-6143-8653","affiliations":[{"raw_affiliation_string":"Mastercard AI Garage, Gurugram, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076286184","display_name":"Siddhartha Asthana","orcid":"https://orcid.org/0000-0002-6798-1240"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siddhartha Asthana","raw_affiliation_strings":["Mastercard AI Garage, Gurugram, India"],"raw_orcid":"https://orcid.org/0000-0002-6798-1240","affiliations":[{"raw_affiliation_string":"Mastercard AI Garage, Gurugram, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5134494290"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.86599543,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"144","last_page":"152"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9896000027656555,"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":0.9896000027656555,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.002099999925121665,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T14347","display_name":"Big Data and Digital Economy","score":0.000699999975040555,"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/graph","display_name":"Graph","score":0.6327999830245972},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5364000201225281},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5135999917984009},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4088999927043915},{"id":"https://openalex.org/keywords/disadvantaged","display_name":"Disadvantaged","score":0.4032000005245209},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.35600000619888306},{"id":"https://openalex.org/keywords/curriculum","display_name":"Curriculum","score":0.35359999537467957}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7257000207901001},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6327999830245972},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5544000267982483},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5364000201225281},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5144000053405762},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5135999917984009},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.412200003862381},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4088999927043915},{"id":"https://openalex.org/C2780623907","wikidata":"https://www.wikidata.org/wiki/Q106394435","display_name":"Disadvantaged","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36010000109672546},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C47177190","wikidata":"https://www.wikidata.org/wiki/Q207137","display_name":"Curriculum","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C137753397","wikidata":"https://www.wikidata.org/wiki/Q2434424","display_name":"Network science","level":3,"score":0.2761000096797943},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2612000107765198}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3799830.3799846","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799846","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3799830.3799846","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799846","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.45192790031433105}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2132022337","https://openalex.org/W2296073425","https://openalex.org/W2949676527","https://openalex.org/W2979683452","https://openalex.org/W3011667710","https://openalex.org/W3048692209","https://openalex.org/W3128443161","https://openalex.org/W3172133997","https://openalex.org/W3210034512","https://openalex.org/W4220900167","https://openalex.org/W4321488441","https://openalex.org/W4382239632","https://openalex.org/W4382466430","https://openalex.org/W7133209885","https://openalex.org/W7133227520","https://openalex.org/W7133237514","https://openalex.org/W7133237556"],"related_works":[],"abstract_inverted_index":{"Curriculum":[0],"Learning":[1],"(CL)":[2],"is":[3,32],"a":[4,61],"training":[5],"strategy":[6],"for":[7,27],"Message-Passing":[8],"Graph":[9,188],"Neural":[10],"Networks":[11],"(MP-GNNs)":[12],"that":[13],"enhances":[14],"performance":[15],"by":[16,144],"progressively":[17],"introducing":[18],"easier":[19],"to":[20,35,84,121],"harder":[21],"examples.":[22],"However,":[23,57],"defining":[24],"sample":[25],"difficulty":[26,55],"CL":[28,189],"on":[29,45,171,175,191],"graph":[30,39,146],"data":[31],"challenging":[33,153],"due":[34],"complex":[36],"dependencies":[37],"in":[38,78,129,182],"structure.":[40],"Prior":[41],"research":[42],"has":[43],"focused":[44],"using":[46],"label":[47],"distribution,":[48],"node":[49,104,110,176],"representation,":[50],"and":[51,88,134,178],"edge":[52],"information":[53,117],"as":[54],"metrics.":[56],"these":[58],"studies":[59],"overlook":[60],"fundamental":[62],"limitation":[63],"affecting":[64],"MP-GNNs:":[65],"structural":[66],"disparity":[67],"among":[68],"nodes.":[69],"It":[70],"restricts":[71],"the":[72,79,97,114,126],"effective":[73],"participation":[74],"of":[75,116],"disadvantaged":[76],"nodes":[77,154],"message":[80],"passing":[81],"process,":[82],"leading":[83],"poor":[85],"classification":[86,177],"accuracy":[87],"overall":[89],"model":[90],"degradation.":[91],"To":[92],"address":[93],"this,":[94],"we":[95],"propose":[96],"Structural":[98],"Difficulty":[99],"Index":[100],"(SDI),":[101],"which":[102,112,124,139],"measures":[103],"influence":[105],"from":[106],"three":[107],"perspectives:":[108],"(1)":[109],"degree,":[111],"captures":[113],"extent":[115],"propagation":[118],"(2)":[119],"proximity":[120],"labeled":[122],"nodes,":[123],"determines":[125],"node\u2019s":[127],"involvement":[128],"weight":[130],"updates":[131],"during":[132],"backpropagation,":[133],"(3)":[135],"neighborhood":[136],"feature":[137],"diversity,":[138],"identifies":[140],"harmful":[141],"heterophily":[142],"caused":[143],"high-frequency":[145],"signals.":[147],"Our":[148,184],"method":[149,185],"gradually":[150],"incorporates":[151],"more":[152],"into":[155],"training,":[156],"enhancing":[157],"MP-GNNs":[158],"without":[159],"modifying":[160],"backbone":[161],"architectures":[162],"or":[163],"requiring":[164],"pre-trained":[165],"models.":[166],"We":[167],"evaluate":[168],"our":[169],"approach":[170],"eight":[172],"benchmark":[173],"datasets":[174,193],"observed":[179],"significant":[180],"improvements":[181],"performance.":[183],"outperforms":[186],"state-of-the-art":[187],"baselines":[190],"six":[192],"across":[194],"various":[195],"MP-GNN":[196],"architectures.":[197]},"counts_by_year":[],"updated_date":"2026-04-29T09:16:38.111599","created_date":"2026-04-25T00:00:00"}
