{"id":"https://openalex.org/W4401864132","doi":"https://doi.org/10.1145/3637528.3671805","title":"Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs","display_name":"Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401864132","doi":"https://doi.org/10.1145/3637528.3671805"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671805","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671805","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671805","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671805","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001934135","display_name":"Hewen Wang","orcid":"https://orcid.org/0000-0002-9757-4347"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Hewen Wang","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040420455","display_name":"Renchi Yang","orcid":"https://orcid.org/0000-0002-7284-3096"},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Renchi Yang","raw_affiliation_strings":["Hong Kong Baptist University, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Hong Kong Baptist University, Hong Kong, China","institution_ids":["https://openalex.org/I141568987"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010903591","display_name":"Xiaokui Xiao","orcid":"https://orcid.org/0000-0003-0914-4580"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Xiaokui Xiao","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5001934135"],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":1.0911,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.81117794,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3081","last_page":"3091"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9962000250816345,"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"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9728000164031982,"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/computer-science","display_name":"Computer science","score":0.6863998174667358},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.6807341575622559},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5919095873832703},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5416861176490784},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5034822821617126},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43548157811164856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35665735602378845}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6863998174667358},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.6807341575622559},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5919095873832703},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5416861176490784},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5034822821617126},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43548157811164856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35665735602378845}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671805","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671805","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671805","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3637528.3671805","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671805","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671805","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4401864132.pdf"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W2022322548","https://openalex.org/W2154851992","https://openalex.org/W2616344221","https://openalex.org/W2767597557","https://openalex.org/W2779570008","https://openalex.org/W2885549115","https://openalex.org/W2890094462","https://openalex.org/W2925395532","https://openalex.org/W2950697450","https://openalex.org/W2954442758","https://openalex.org/W2962756421","https://openalex.org/W2971504002","https://openalex.org/W3007813770","https://openalex.org/W3034374044","https://openalex.org/W3104097132","https://openalex.org/W3117298090","https://openalex.org/W3122089757","https://openalex.org/W3126391297","https://openalex.org/W3152560880","https://openalex.org/W3154221903","https://openalex.org/W3171903345","https://openalex.org/W3173804797","https://openalex.org/W3175164304","https://openalex.org/W3201572672","https://openalex.org/W3208180646","https://openalex.org/W3215602957","https://openalex.org/W4206256971","https://openalex.org/W4221166604","https://openalex.org/W4224313061","https://openalex.org/W4226065257","https://openalex.org/W4226208698","https://openalex.org/W4231916799","https://openalex.org/W4281868159","https://openalex.org/W4313855747","https://openalex.org/W4322731006","https://openalex.org/W4328051715","https://openalex.org/W4367046776","https://openalex.org/W4384773367","https://openalex.org/W4385567653","https://openalex.org/W4387439537","https://openalex.org/W4389539847","https://openalex.org/W4390437720","https://openalex.org/W4393212306","https://openalex.org/W6797907005"],"related_works":["https://openalex.org/W2371352078","https://openalex.org/W2953461625","https://openalex.org/W2077383796","https://openalex.org/W2080136900","https://openalex.org/W2999799752","https://openalex.org/W3088754131","https://openalex.org/W3038848193","https://openalex.org/W4285218279","https://openalex.org/W4386136067","https://openalex.org/W4286858940"],"abstract_inverted_index":{"Graph":[0],"representation":[1],"learning":[2,84],"(GRL)":[3],"is":[4,91],"to":[5,51,81,94,97,136,253],"encode":[6],"graph":[7],"elements":[8],"into":[9,201],"informative":[10],"vector":[11],"representations,":[12,45],"which":[13,46],"can":[14,236],"be":[15,48],"used":[16],"in":[17,29,56,70,77,122,207,212,228,245,249],"downstream":[18],"tasks":[19,55],"for":[20,157,175],"analyzing":[21],"graph-structured":[22],"data":[23],"and":[24,73,101,120,139,163,209,247],"has":[25,133],"seen":[26],"extensive":[27],"applications":[28],"various":[30],"domains.":[31],"However,":[32],"the":[33,63,95,99,105,111,169,203,221,224,254],"majority":[34],"of":[35,107,114,181,184,223,241],"extant":[36],"studies":[37],"on":[38,88,160,216],"GRL":[39],"are":[40],"geared":[41],"towards":[42],"generating":[43],"node":[44,117],"cannot":[47],"readily":[49],"employed":[50],"perform":[52],"edge-based":[53],"analytics":[54],"edge-attributed":[57],"bipartite":[58,123],"graphs":[59,90],"(EABGs)":[60],"that":[61],"pervade":[62],"real":[64,218],"world,":[65],"e.g.,":[66],"spam":[67],"review":[68],"detection":[69],"customer-product":[71],"reviews":[72],"identifying":[74],"fraudulent":[75],"transactions":[76],"user-merchant":[78],"networks.":[79],"Compared":[80],"node-wise":[82],"GRL,":[83],"edge":[85,176,230],"representations":[86,177],"(ERL)":[87],"such":[89],"challenging":[92],"due":[93],"need":[96],"incorporate":[98],"structure":[100],"attribute":[102],"semantics":[103],"from":[104,144,205],"perspective":[106],"edges":[108],"while":[109],"considering":[110],"separate":[112],"influence":[113],"two":[115],"heterogeneous":[116],"sets":[118],"U":[119,208],"V":[121,210],"graphs.":[124],"To":[125],"our":[126],"knowledge,":[127],"despite":[128],"its":[129],"importance,":[130],"limited":[131],"research":[132],"been":[134],"devoted":[135],"this":[137,149],"frontier,":[138],"existing":[140],"workarounds":[141],"all":[142],"suffer":[143],"sub-par":[145],"results.Motivated":[146],"by":[147,199],"this,":[148],"paper":[150],"designs":[151],"EAGLE,":[152],"an":[153,161],"effective":[154],"ERL":[155],"method":[156],"EABGs.":[158],"Building":[159],"in-depth":[162],"rigorous":[164],"theoretical":[165],"analysis,":[166],"we":[167],"propose":[168],"factorized":[170],"feature":[171],"propagation":[172],"(FFP)":[173],"scheme":[174],"with":[178],"adequate":[179],"incorporation":[180],"long-range":[182],"dependencies":[183],"edges/features":[185],"without":[186],"incurring":[187],"tremendous":[188],"computation":[189],"overheads.":[190],"We":[191],"further":[192],"ameliorate":[193],"FFP":[194,198],"as":[195],"a":[196,238],"dual-view":[197],"taking":[200],"account":[202],"influences":[204],"nodes":[206],"severally":[211],"ERL.":[213],"Extensive":[214],"experiments":[215],"5":[217],"datasets":[219],"showcase":[220],"effectiveness":[222],"proposed":[225],"EAGLE":[226,235],"models":[227],"semi-supervised":[229],"classification":[231],"tasks.":[232],"In":[233],"particular,":[234],"attain":[237],"considerable":[239],"gain":[240],"at":[242],"most":[243],"38.11%":[244],"AP":[246],"1.86%":[248],"AUC":[250],"when":[251],"compared":[252],"best":[255],"baselines.":[256]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}