{"id":"https://openalex.org/W4280526979","doi":"https://doi.org/10.24963/ijcai.2022/336","title":"Table2Graph: Transforming Tabular Data to Unified Weighted Graph","display_name":"Table2Graph: Transforming Tabular Data to Unified Weighted Graph","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W4280526979","doi":"https://doi.org/10.24963/ijcai.2022/336"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2022/336","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/336","pdf_url":"https://www.ijcai.org/proceedings/2022/0336.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","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 Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2022/0336.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071607114","display_name":"Kaixiong Zhou","orcid":"https://orcid.org/0000-0001-5226-8736"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kaixiong Zhou","raw_affiliation_strings":["Rice University","Department of Computer Science, Rice University"],"affiliations":[{"raw_affiliation_string":"Rice University","institution_ids":["https://openalex.org/I74775410"]},{"raw_affiliation_string":"Department of Computer Science, Rice University","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101916543","display_name":"Zirui Liu","orcid":"https://orcid.org/0000-0001-9062-6565"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zirui Liu","raw_affiliation_strings":["Rice University","Department of Computer Science, Rice University"],"affiliations":[{"raw_affiliation_string":"Rice University","institution_ids":["https://openalex.org/I74775410"]},{"raw_affiliation_string":"Department of Computer Science, Rice University","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403698","display_name":"Rui Chen","orcid":"https://orcid.org/0009-0001-7120-4209"},"institutions":[{"id":"https://openalex.org/I4210133173","display_name":"Research!America (United States)","ror":"https://ror.org/044pgyv50","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133173"]},{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Chen","raw_affiliation_strings":["Samsung Research America"],"affiliations":[{"raw_affiliation_string":"Samsung Research America","institution_ids":["https://openalex.org/I4210101778","https://openalex.org/I4210133173"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100361415","display_name":"Li Li","orcid":"https://orcid.org/0009-0002-8638-457X"},"institutions":[{"id":"https://openalex.org/I4210133173","display_name":"Research!America (United States)","ror":"https://ror.org/044pgyv50","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133173"]},{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Li","raw_affiliation_strings":["Samsung Research America"],"affiliations":[{"raw_affiliation_string":"Samsung Research America","institution_ids":["https://openalex.org/I4210101778","https://openalex.org/I4210133173"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027525032","display_name":"Soo-Hyun Choi","orcid":"https://orcid.org/0000-0001-5768-9978"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soo-Hyun Choi","raw_affiliation_strings":["Samsung Electronics"],"affiliations":[{"raw_affiliation_string":"Samsung Electronics","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068477431","display_name":"Xia Hu","orcid":"https://orcid.org/0000-0003-2234-3226"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xia Hu","raw_affiliation_strings":["Rice University","Department of Computer Science, Rice University"],"affiliations":[{"raw_affiliation_string":"Rice University","institution_ids":["https://openalex.org/I74775410"]},{"raw_affiliation_string":"Department of Computer Science, Rice University","institution_ids":["https://openalex.org/I74775410"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5071607114"],"corresponding_institution_ids":["https://openalex.org/I74775410"],"apc_list":null,"apc_paid":null,"fwci":1.612,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.85556986,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2420","last_page":"2426"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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/T13702","display_name":"Machine Learning in Healthcare","score":0.958899974822998,"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/computer-science","display_name":"Computer science","score":0.7065334320068359},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.6246294379234314},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5976977944374084},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4973471462726593},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49410417675971985},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.48787498474121094},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.48191216588020325},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44163352251052856},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38650041818618774},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3611528277397156},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3210908770561218}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7065334320068359},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.6246294379234314},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5976977944374084},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4973471462726593},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49410417675971985},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.48787498474121094},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.48191216588020325},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44163352251052856},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38650041818618774},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3611528277397156},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3210908770561218},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2022/336","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/336","pdf_url":"https://www.ijcai.org/proceedings/2022/0336.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","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 Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2022/336","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/336","pdf_url":"https://www.ijcai.org/proceedings/2022/0336.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","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 Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4280526979.pdf","grobid_xml":"https://content.openalex.org/works/W4280526979.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W2048231652","https://openalex.org/W2100235918","https://openalex.org/W2155027007","https://openalex.org/W2155279768","https://openalex.org/W2217007515","https://openalex.org/W2292575322","https://openalex.org/W2553303224","https://openalex.org/W2604662567","https://openalex.org/W2605350416","https://openalex.org/W2749348810","https://openalex.org/W2802187397","https://openalex.org/W2885311373","https://openalex.org/W2898085636","https://openalex.org/W2913194878","https://openalex.org/W2959334635","https://openalex.org/W2963925437","https://openalex.org/W2964015378","https://openalex.org/W2964052347","https://openalex.org/W3018498408","https://openalex.org/W3034618665","https://openalex.org/W3035659929","https://openalex.org/W3093945404","https://openalex.org/W3103515126","https://openalex.org/W3104439459","https://openalex.org/W3105140685","https://openalex.org/W3170187879","https://openalex.org/W3178159063","https://openalex.org/W4232449914","https://openalex.org/W4294558607","https://openalex.org/W4307123371","https://openalex.org/W4309609199"],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2793211469","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2942366970","https://openalex.org/W2807634898","https://openalex.org/W1692008701","https://openalex.org/W2597588799","https://openalex.org/W4360593462","https://openalex.org/W4383602045"],"abstract_inverted_index":{"Learning":[0],"useful":[1],"interactions":[2,20,121,148],"between":[3],"input":[4],"features":[5],"is":[6,26,78,153],"crucial":[7],"for":[8,61],"tabular":[9,129],"data":[10],"modeling.":[11],"Recent":[12],"efforts":[13],"start":[14],"to":[15,55,71,95,101,116,143,156],"explicitly":[16],"model":[17,81,117],"the":[18,33,57,65,74,97,112,118,124,128,134,139,145,158,174,184],"feature":[19,25,59,98,120,147,188],"with":[21],"graph,":[22,136],"where":[23],"each":[24,62],"treated":[27],"as":[28,107],"an":[29],"individual":[30],"node.":[31],"However,":[32],"existing":[34],"graph":[35,44,67,76,114,160],"construction":[36,77],"methods":[37],"either":[38],"heuristically":[39],"formulate":[40],"a":[41,91,103,108,168],"fixed":[42,66],"feature-interaction":[43],"based":[45],"on":[46],"specific":[47],"domain":[48],"knowledge,":[49],"or":[50],"simply":[51],"apply":[52],"attention":[53],"function":[54],"compute":[56],"pairwise":[58],"similarities":[60],"sample.":[63],"While":[64],"may":[68],"be":[69],"sub-optimal":[70],"downstream":[72],"tasks,":[73],"sample-wise":[75],"time-consuming":[79],"during":[80],"training":[82],"and":[83,176,187],"inference.":[84],"To":[85,131],"tackle":[86],"these":[87],"issues,":[88],"we":[89,137],"propose":[90],"framework":[92],"named":[93],"Table2Graph":[94],"transform":[96],"interaction":[99,189],"modeling":[100],"learning":[102,141],"unified":[104,113,135],"graph.":[105],"Represented":[106],"probability":[109],"adjacency":[110],"matrix,":[111],"learns":[115],"key":[119,146],"shared":[122],"by":[123],"diverse":[125],"samples":[126],"in":[127,167,181],"data.":[130],"well":[132],"optimize":[133],"employ":[138],"reinforcement":[140],"policy":[142],"capture":[144],"stably.":[149],"A":[150],"sparsity":[151],"constraint":[152],"also":[154],"proposed":[155],"regularize":[157],"learned":[159],"from":[161],"being":[162],"overly-sparse/smooth.":[163],"The":[164],"experimental":[165],"results":[166],"variety":[169],"of":[170,178,183],"real-world":[171],"applications":[172],"demonstrate":[173],"effectiveness":[175],"efficiency":[177],"our":[179],"Table2Graph,":[180],"terms":[182],"prediction":[185],"accuracy":[186],"detection.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
