{"id":"https://openalex.org/W3166757600","doi":"https://doi.org/10.1145/3447548.3467383","title":"Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups","display_name":"Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups","publication_year":2021,"publication_date":"2021-08-13","ids":{"openalex":"https://openalex.org/W3166757600","doi":"https://doi.org/10.1145/3447548.3467383","mag":"3166757600"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467383","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5085419775","display_name":"Shengming Zhang","orcid":"https://orcid.org/0009-0001-4549-3262"},"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":"Shengming Zhang","raw_affiliation_strings":["Rutgers University, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, Newark, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002503342","display_name":"Hao Zhong","orcid":"https://orcid.org/0000-0002-4302-9183"},"institutions":[{"id":"https://openalex.org/I4210149577","display_name":"ESCP Business School","ror":"https://ror.org/040hhjv66","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210149577"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hao Zhong","raw_affiliation_strings":["ESCP Business School, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ESCP Business School, Paris, France","institution_ids":["https://openalex.org/I4210149577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049462386","display_name":"Zixuan Yuan","orcid":"https://orcid.org/0000-0003-1197-0347"},"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":"Zixuan Yuan","raw_affiliation_strings":["Rutgers University, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, Newark, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101862104","display_name":"Hui Xiong","orcid":"https://orcid.org/0000-0001-6016-6465"},"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":"Hui Xiong","raw_affiliation_strings":["Rutgers University, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, Newark, 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":0.8792,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.75980422,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2202","last_page":"2211"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9976000189781189,"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":0.9976000189781189,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.920199990272522,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7836164236068726},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6909239292144775},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6136205196380615},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5086058378219604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.459989458322525},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44903090596199036},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43848860263824463},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.41349726915359497},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3719879686832428}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7836164236068726},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6909239292144775},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6136205196380615},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5086058378219604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.459989458322525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44903090596199036},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43848860263824463},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.41349726915359497},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3719879686832428},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447548.3467383","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G4205862728","display_name":null,"funder_award_id":"III-2006387 IIS 1814510 IIS-2040799","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":32,"referenced_works":["https://openalex.org/W1487588218","https://openalex.org/W1501299699","https://openalex.org/W1566068427","https://openalex.org/W2048832996","https://openalex.org/W2120516694","https://openalex.org/W2121075864","https://openalex.org/W2277082599","https://openalex.org/W2563980602","https://openalex.org/W2585499563","https://openalex.org/W2594908799","https://openalex.org/W2604314403","https://openalex.org/W2743104969","https://openalex.org/W2757217459","https://openalex.org/W2897756463","https://openalex.org/W2911286998","https://openalex.org/W2945827377","https://openalex.org/W2964912499","https://openalex.org/W2965857891","https://openalex.org/W2970066309","https://openalex.org/W2973163739","https://openalex.org/W2991360673","https://openalex.org/W3004259048","https://openalex.org/W3004507689","https://openalex.org/W3012871709","https://openalex.org/W3101553402","https://openalex.org/W3103513278","https://openalex.org/W3112180085","https://openalex.org/W3123946679","https://openalex.org/W3154419072","https://openalex.org/W4238981357","https://openalex.org/W4240636403","https://openalex.org/W4384786839"],"related_works":["https://openalex.org/W2389214306","https://openalex.org/W4235240664","https://openalex.org/W2965083567","https://openalex.org/W1838576100","https://openalex.org/W2095886385","https://openalex.org/W2089704382","https://openalex.org/W1983399550","https://openalex.org/W97075385","https://openalex.org/W2357523926","https://openalex.org/W2889616422"],"abstract_inverted_index":{"It":[0],"is":[1,103,197],"critical":[2],"and":[3,44,119,148,204,212,243],"important":[4],"for":[5,27,79,95],"venture":[6,45],"investors":[7],"to":[8,22,104,110,138,167,186,199,231],"find":[9],"high-potential":[10,98,161],"startups":[11,31,125],"at":[12],"their":[13],"early":[14],"stages.":[15],"Indeed,":[16],"many":[17],"efforts":[18],"have":[19],"been":[20],"made":[21],"study":[23],"the":[24,28,33,37,56,68,97,121,143,160,169,172,210,213,216,250],"key":[25],"factors":[26],"success":[29],"of":[30,36,41,50,64,252],"through":[32,115,126],"topological":[34,58],"analysis":[35,59],"heterogeneous":[38,65,144,237],"information":[39,145,152,170,238],"network":[40,146],"people,":[42],"startup,":[43],"firms":[46],"or":[47],"representation":[48,72],"learning":[49,73],"latent":[51],"startup":[52,113],"profile":[53],"features.":[54],"However,":[55],"existing":[57],"lacks":[60],"an":[61,178],"in-depth":[62],"understanding":[63],"information.":[66],"Also,":[67],"approach":[69],"based":[70],"on":[71,76,246],"heavily":[74],"relies":[75],"domain-specific":[77],"knowledge":[78],"feature":[80],"selections.":[81],"Instead,":[82],"in":[83],"this":[84],"paper,":[85],"we":[86,133,164,221],"propose":[87],"aScalable":[88],"Heterogeneous":[89],"Graph":[90],"Markov":[91,183],"Neural":[92],"Network":[93],"(SHGMNN)":[94],"identifying":[96],"startups.":[99],"The":[100,207],"general":[101],"idea":[102],"use":[105],"graph":[106,156,233],"neural":[107],"networks":[108],"(GNN)":[109],"learn":[111],"effective":[112],"representations":[114],"end-to-end":[116],"efficient":[117],"training":[118],"model":[120],"label":[122,188],"dependency":[123],"among":[124],"Maximum":[127],"A":[128],"Posterior":[129],"(MAP)":[130],"inference.":[131],"Specifically,":[132],"first":[134],"define":[135],"different":[136],"metapaths":[137],"capture":[139],"various":[140],"semantics":[141],"over":[142,171,181,235],"(HIN)":[147],"aggregate":[149],"all":[150],"semantic":[151],"into":[153],"a":[154,191,223,226],"summated":[155,173],"structure.":[157],"To":[158],"predict":[159],"early-stage":[162],"startups,":[163],"introduce":[165],"GNN":[166,205,224],"diffuse":[168],"graph.":[174],"We":[175],"then":[176],"adopt":[177],"MAP":[179,202],"inference":[180,203],"Hinge-Loss":[182],"Random":[184],"Fields":[185],"enforce":[187],"dependency.":[189],"Here,":[190],"pseudolikelihood":[192],"variational":[193],"expectation-maximization":[194],"(EM)":[195],"framework":[196],"incorporated":[198],"optimize":[200],"both":[201],"iteratively:":[206],"E-step":[208],"calculates":[209],"inference,":[211],"M-step":[214],"updates":[215],"GNN.":[217],"For":[218],"efficiency":[219],"concerns,":[220],"develop":[222],"with":[225],"lightweight":[227],"linear":[228],"diffusion":[229],"architecture":[230],"perform":[232],"propagation":[234],"web-scale":[236],"networks.":[239],"Finally,":[240],"extensive":[241],"experiments":[242],"case":[244],"studies":[245],"real-world":[247],"datasets":[248],"demonstrate":[249],"superiority":[251],"SHGMNN.":[253]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}