{"id":"https://openalex.org/W4301019171","doi":"https://doi.org/10.14778/3551793.3551831","title":"Algorithm and system co-design for efficient subgraph-based graph representation learning","display_name":"Algorithm and system co-design for efficient subgraph-based graph representation learning","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W4301019171","doi":"https://doi.org/10.14778/3551793.3551831"},"language":"en","primary_location":{"id":"doi:10.14778/3551793.3551831","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3551793.3551831","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-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/A5004119013","display_name":"Haoteng Yin","orcid":"https://orcid.org/0000-0002-9277-6150"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoteng Yin","raw_affiliation_strings":["Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071515223","display_name":"Muhan Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Muhan Zhang","raw_affiliation_strings":["Peking University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001319317","display_name":"Yanbang Wang","orcid":"https://orcid.org/0000-0001-9177-413X"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanbang Wang","raw_affiliation_strings":["Cornell University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100336334","display_name":"Jianguo Wang","orcid":"https://orcid.org/0000-0002-4935-9116"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianguo Wang","raw_affiliation_strings":["Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100620216","display_name":"Li Pan","orcid":"https://orcid.org/0000-0002-0424-9845"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pan Li","raw_affiliation_strings":["Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.4683,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.9344446,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"15","issue":"11","first_page":"2788","last_page":"2796"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.993399977684021,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9822999835014343,"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/scalability","display_name":"Scalability","score":0.8001705408096313},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7231591939926147},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.543999969959259},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5319170355796814},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5165285468101501},{"id":"https://openalex.org/keywords/graph-factorization","display_name":"Graph factorization","score":0.5093892216682434},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4565432071685791},{"id":"https://openalex.org/keywords/homogeneous","display_name":"Homogeneous","score":0.43004170060157776},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38376039266586304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3693099915981293},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29595810174942017},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17291390895843506},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.10792788863182068},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.10511809587478638},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08234569430351257}],"concepts":[{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.8001705408096313},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7231591939926147},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.543999969959259},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5319170355796814},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5165285468101501},{"id":"https://openalex.org/C128115575","wikidata":"https://www.wikidata.org/wiki/Q5597083","display_name":"Graph factorization","level":5,"score":0.5093892216682434},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4565432071685791},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.43004170060157776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38376039266586304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3693099915981293},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29595810174942017},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17291390895843506},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.10792788863182068},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.10511809587478638},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08234569430351257},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3551793.3551831","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3551793.3551831","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2154851992","https://openalex.org/W2807021761","https://openalex.org/W2912083425","https://openalex.org/W2943373497","https://openalex.org/W2945827377","https://openalex.org/W2962756421","https://openalex.org/W2962810718","https://openalex.org/W2964571482","https://openalex.org/W2971933740","https://openalex.org/W2990138404","https://openalex.org/W3002924435","https://openalex.org/W3037699692","https://openalex.org/W3081214609","https://openalex.org/W3084983693","https://openalex.org/W3086238199","https://openalex.org/W3100848837","https://openalex.org/W3101553402","https://openalex.org/W3104097132","https://openalex.org/W3164473340","https://openalex.org/W3165484655","https://openalex.org/W3190799236","https://openalex.org/W3197982863","https://openalex.org/W4206482253","https://openalex.org/W4224318899","https://openalex.org/W4232932184"],"related_works":["https://openalex.org/W2389214306","https://openalex.org/W2965083567","https://openalex.org/W4235240664","https://openalex.org/W1838576100","https://openalex.org/W2757182831","https://openalex.org/W2095886385","https://openalex.org/W2133886031","https://openalex.org/W2347984675","https://openalex.org/W2365944862","https://openalex.org/W3124782647"],"abstract_inverted_index":{"Subgraph-based":[0],"graph":[1,18],"representation":[2],"learning":[3,83],"(SGRL)":[4],"has":[5,23],"been":[6],"recently":[7],"proposed":[8],"to":[9,68,99,138,154],"deal":[10],"with":[11,122,145],"some":[12],"fundamental":[13],"challenges":[14],"encountered":[15],"by":[16,80],"canonical":[17,63,155],"neural":[19],"networks":[20],"(GNNs),":[21],"and":[22,36,85,95,110,119,126,131],"demonstrated":[24],"advantages":[25],"in":[26],"many":[27],"important":[28],"data":[29],"science":[30],"applications":[31],"such":[32],"as":[33],"link,":[34],"relation":[35],"motif":[37],"prediction.":[38],"However,":[39],"current":[40],"SGRL":[41,79,139],"approaches":[42],"suffer":[43],"from":[44],"scalability":[45,132],"issues":[46],"since":[47],"they":[48],"require":[49],"extracting":[50],"subgraphs":[51,94],"for":[52,77],"each":[53],"training":[54],"or":[55,147],"test":[56],"query.":[57],"Recent":[58],"solutions":[59],"that":[60],"scale":[61],"up":[62],"GNNs":[64],"may":[65],"not":[66],"apply":[67],"SGRL.":[69],"Here,":[70],"we":[71],"propose":[72],"a":[73],"novel":[74],"framework":[75],"SUREL":[76,89,141,157],"scalable":[78],"co-designing":[81],"the":[82,97,105,129],"algorithm":[84],"its":[86],"system":[87],"support.":[88],"adopts":[90],"walk-based":[91],"decomposition":[92],"of":[93,107,124,133],"reuses":[96],"walks":[98],"form":[100],"subgraphs,":[101],"which":[102],"substantially":[103],"reduces":[104],"redundancy":[106],"subgraph":[108],"extraction":[109],"supports":[111],"parallel":[112],"computation.":[113],"Experiments":[114],"over":[115],"six":[116],"homogeneous,":[117],"heterogeneous":[118],"higher-order":[120],"graphs":[121],"millions":[123],"nodes":[125],"edges":[127],"demonstrate":[128],"effectiveness":[130],"SUREL.":[134],"In":[135],"particular,":[136],"compared":[137,153],"baselines,":[140],"achieves":[142,158],"10X":[143],"speed-up":[144],"comparable":[146],"even":[148],"better":[149],"prediction":[150,160],"performance;":[151],"while":[152],"GNNs,":[156],"50%":[159],"accuracy":[161],"improvement.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2022-10-04T00:00:00"}
