{"id":"https://openalex.org/W4213273382","doi":"https://doi.org/10.1145/3488560.3498410","title":"Graph Collaborative Reasoning","display_name":"Graph Collaborative Reasoning","publication_year":2022,"publication_date":"2022-02-11","ids":{"openalex":"https://openalex.org/W4213273382","doi":"https://doi.org/10.1145/3488560.3498410"},"language":"en","primary_location":{"id":"doi:10.1145/3488560.3498410","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488560.3498410","pdf_url":null,"source":{"id":"https://openalex.org/S4363608885","display_name":"Proceedings of the Fifteenth ACM International Conference on Web Search 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 Fifteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2112.13705","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029295590","display_name":"Hanxiong Chen","orcid":"https://orcid.org/0000-0002-3854-2732"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hanxiong Chen","raw_affiliation_strings":["Rutgers University, New Brunswick, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101668890","display_name":"Yunqi Li","orcid":"https://orcid.org/0000-0002-9482-9922"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunqi Li","raw_affiliation_strings":["Rutgers University, New Brunswick, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038518790","display_name":"Shaoyun Shi","orcid":"https://orcid.org/0000-0002-1524-7132"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoyun Shi","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100600807","display_name":"Shuchang Liu","orcid":"https://orcid.org/0000-0002-1440-911X"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuchang Liu","raw_affiliation_strings":["Rutgers University, New Brunswick, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004145814","display_name":"He Zhu","orcid":"https://orcid.org/0000-0001-9606-150X"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"He Zhu","raw_affiliation_strings":["Rutgers University, New Brunswick, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087294988","display_name":"Yongfeng Zhang","orcid":"https://orcid.org/0000-0002-9519-9774"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yongfeng Zhang","raw_affiliation_strings":["Rutgers University, New Brunswick, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"75","last_page":"84"},"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/T10028","display_name":"Topic Modeling","score":0.9986000061035156,"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":0.9930999875068665,"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/computer-science","display_name":"Computer science","score":0.7640388011932373},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5209556818008423},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4884313642978668},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48698553442955017},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.44756004214286804},{"id":"https://openalex.org/keywords/logical-reasoning","display_name":"Logical reasoning","score":0.43085065484046936},{"id":"https://openalex.org/keywords/reasoning-system","display_name":"Reasoning system","score":0.43057799339294434},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.380990594625473},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.2745969295501709},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.22343069314956665}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7640388011932373},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5209556818008423},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4884313642978668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48698553442955017},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.44756004214286804},{"id":"https://openalex.org/C43971567","wikidata":"https://www.wikidata.org/wiki/Q3142865","display_name":"Logical reasoning","level":2,"score":0.43085065484046936},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.43057799339294434},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.380990594625473},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.2745969295501709},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.22343069314956665}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3488560.3498410","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488560.3498410","pdf_url":null,"source":{"id":"https://openalex.org/S4363608885","display_name":"Proceedings of the Fifteenth ACM International Conference on Web Search 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 Fifteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2112.13705","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2112.13705","pdf_url":"https://arxiv.org/pdf/2112.13705","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2112.13705","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2112.13705","pdf_url":"https://arxiv.org/pdf/2112.13705","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1309303346","display_name":null,"funder_award_id":"IIS-1910154, IIS-2007907, IIS-2046457","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W205829674","https://openalex.org/W398859631","https://openalex.org/W1498436455","https://openalex.org/W1522301498","https://openalex.org/W1533230146","https://openalex.org/W2027731328","https://openalex.org/W2054141820","https://openalex.org/W2127795553","https://openalex.org/W2140310134","https://openalex.org/W2184957013","https://openalex.org/W2250342289","https://openalex.org/W2250635077","https://openalex.org/W2274308990","https://openalex.org/W2283196293","https://openalex.org/W2296268288","https://openalex.org/W2432356473","https://openalex.org/W2489487449","https://openalex.org/W2563063592","https://openalex.org/W2593084806","https://openalex.org/W2604314403","https://openalex.org/W2606780347","https://openalex.org/W2624407581","https://openalex.org/W2728059831","https://openalex.org/W2774837955","https://openalex.org/W2781216040","https://openalex.org/W2781528640","https://openalex.org/W2945827670","https://openalex.org/W2949434543","https://openalex.org/W2949972983","https://openalex.org/W2951105272","https://openalex.org/W2962869320","https://openalex.org/W2963290083","https://openalex.org/W2963359213","https://openalex.org/W2963469133","https://openalex.org/W2964015378","https://openalex.org/W2966459188","https://openalex.org/W2970583209","https://openalex.org/W3003760334","https://openalex.org/W3003897916","https://openalex.org/W3021815340","https://openalex.org/W3024683329","https://openalex.org/W3093531652","https://openalex.org/W3094076810","https://openalex.org/W3094587024","https://openalex.org/W3096588269","https://openalex.org/W3099387504","https://openalex.org/W3100278010","https://openalex.org/W3153088333","https://openalex.org/W4287706147","https://openalex.org/W4294558607","https://openalex.org/W4297733535"],"related_works":["https://openalex.org/W2389872472","https://openalex.org/W2311306072","https://openalex.org/W1864329966","https://openalex.org/W1558526662","https://openalex.org/W4385573007","https://openalex.org/W312725877","https://openalex.org/W4226458493","https://openalex.org/W4226145644","https://openalex.org/W2355314240","https://openalex.org/W4385573525"],"abstract_inverted_index":{"Graphs":[0],"can":[1,108,142],"represent":[2],"relational":[3,115],"information":[4,73,113],"among":[5],"entities":[6],"and":[7,21,35,77,88,167,180,204,212],"graph":[8,131,214],"structures":[9],"are":[10,47,56,81],"widely":[11],"used":[12,209],"in":[13,30,98,183],"many":[14,45],"intelligent":[15],"tasks":[16,199],"such":[17,200],"as":[18,201],"search,":[19],"recommendation,":[20],"question":[22],"answering.":[23],"However,":[24],"most":[25],"of":[26,70,191],"the":[27,52,71,110,138,159,164,173,189],"graph-structured":[28],"data":[29],"practice":[31],"suffer":[32],"from":[33,74,119],"incompleteness,":[34],"thus":[36],"link":[37,50,65,112,139,202],"prediction":[38,140,203],"becomes":[39],"an":[40],"important":[41],"research":[42],"problem.":[43,150],"Though":[44],"models":[46,80],"proposed":[48],"for":[49,114],"prediction,":[51],"following":[53],"two":[54],"problems":[55],"still":[57],"less":[58],"explored:":[59],"(1)":[60],"Most":[61],"methods":[62],"model":[63,174],"each":[64],"independently":[66],"without":[67],"making":[68],"use":[69,109,168],"rich":[72],"relevant":[75],"links,":[76],"(2)":[78],"existing":[79],"mostly":[82],"designed":[83],"based":[84,206],"on":[85,117,197,207],"associative":[86],"learning":[87,179],"do":[89],"not":[90],"take":[91],"reasoning":[92,116,121,149,182,216],"into":[93,133,145],"consideration.":[94],"With":[95],"these":[96],"concerns,":[97],"this":[99],"paper,":[100],"we":[101,194],"propose":[102],"Graph":[103],"Collaborative":[104],"Reasoning":[105],"(GCR),":[106],"which":[107,176],"neighbor":[111],"graphs":[118],"logical":[120,134,153,165],"perspectives.":[122],"We":[123,151],"provide":[124],"a":[125,130,146,184],"simple":[126],"approach":[127,217],"to":[128,157,163,170],"translate":[129],"structure":[132],"expressions":[135],"so":[136],"that":[137],"task":[141],"be":[143],"converted":[144],"neural":[147,155],"logic":[148],"apply":[152],"constrained":[154],"modules":[156],"build":[158],"network":[160],"architecture":[161],"according":[162],"expression":[166],"backpropagation":[169],"efficiently":[171],"learn":[172],"parameters,":[175],"bridges":[177],"differentiable":[178],"symbolic":[181],"unified":[185],"architecture.":[186],"To":[187],"show":[188],"effectiveness":[190],"our":[192,213],"work,":[193],"conduct":[195],"experiments":[196],"graph-related":[198],"recommendation":[205],"commonly":[208],"benchmark":[210],"datasets,":[211],"collaborative":[215],"achieves":[218],"state-of-the-art":[219],"performance.":[220]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":8}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
