{"id":"https://openalex.org/W4221093201","doi":"https://doi.org/10.1145/3487553.3524728","title":"SchemaWalk: Schema Aware Random Walks for Heterogeneous Graph Embedding","display_name":"SchemaWalk: Schema Aware Random Walks for Heterogeneous Graph Embedding","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4221093201","doi":"https://doi.org/10.1145/3487553.3524728"},"language":"en","primary_location":{"id":"doi:10.1145/3487553.3524728","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3487553.3524728","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3487553.3524728","source":{"id":"https://openalex.org/S4363608846","display_name":"Companion Proceedings of the Web Conference 2022","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the Web Conference 2022","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3487553.3524728","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081446545","display_name":"Ahmed E. Samy","orcid":"https://orcid.org/0000-0002-5392-6531"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ahmed E. Samy","raw_affiliation_strings":["EECS, KTH Royal Institute of Technology, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS, KTH Royal Institute of Technology, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088007626","display_name":"Lodovico Giaretta","orcid":"https://orcid.org/0000-0002-0223-8907"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Lodovico Giaretta","raw_affiliation_strings":["EECS, KTH Royal Institute of Technology, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS, KTH Royal Institute of Technology, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036955634","display_name":"Zekarias T. Kefato","orcid":"https://orcid.org/0000-0001-7898-0879"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Zekarias T. Kefato","raw_affiliation_strings":["EECS, KTH Royal Institute of Technology, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS, KTH Royal Institute of Technology, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041411651","display_name":"\u0160ar\u016bnas Girdzijauskas","orcid":"https://orcid.org/0000-0003-4516-7317"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"\u0160ar\u016bnas Girdzijauskas","raw_affiliation_strings":["EECS, KTH Royal Institute of Technology, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS, KTH Royal Institute of Technology, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":null,"fwci":1.1806,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80058042,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1157","last_page":"1166"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9908000230789185,"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":0.984000027179718,"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":0.6863365769386292},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6836295127868652},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.6678057312965393},{"id":"https://openalex.org/keywords/schema","display_name":"Schema (genetic algorithms)","score":0.5767531394958496},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5656514167785645},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5439337491989136},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4901309907436371},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4300978183746338},{"id":"https://openalex.org/keywords/social-connectedness","display_name":"Social connectedness","score":0.4258568286895752},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4134748578071594},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29456642270088196},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22229528427124023},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20062649250030518},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10206213593482971}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6863365769386292},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6836295127868652},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.6678057312965393},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.5767531394958496},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5656514167785645},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5439337491989136},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4901309907436371},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4300978183746338},{"id":"https://openalex.org/C201943243","wikidata":"https://www.wikidata.org/wiki/Q7551008","display_name":"Social connectedness","level":2,"score":0.4258568286895752},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4134748578071594},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29456642270088196},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22229528427124023},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20062649250030518},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10206213593482971},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3487553.3524728","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3487553.3524728","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3487553.3524728","source":{"id":"https://openalex.org/S4363608846","display_name":"Companion Proceedings of the Web Conference 2022","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the Web Conference 2022","raw_type":"proceedings-article"},{"id":"pmh:oai:zenodo.org:6384991","is_oa":true,"landing_page_url":"https://zenodo.org/record/6384991","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"doi:10.1145/3487553.3524728","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3487553.3524728","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3487553.3524728","source":{"id":"https://openalex.org/S4363608846","display_name":"Companion Proceedings of the Web Conference 2022","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the Web Conference 2022","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1098431030","display_name":"RAIS: Real-time Analytics for the Internet of Sports","funder_award_id":"813162","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G434337789","display_name":null,"funder_award_id":"HORIZON2020","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4221093201.pdf","grobid_xml":"https://content.openalex.org/works/W4221093201.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1529533208","https://openalex.org/W1602136775","https://openalex.org/W1888005072","https://openalex.org/W2145658888","https://openalex.org/W2154851992","https://openalex.org/W2329660289","https://openalex.org/W2743104969","https://openalex.org/W2911840101","https://openalex.org/W2952343887","https://openalex.org/W2962756421","https://openalex.org/W2963707260","https://openalex.org/W2984834462","https://openalex.org/W3094411093","https://openalex.org/W3104097132","https://openalex.org/W4238452917"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W3038102983","https://openalex.org/W2950907416","https://openalex.org/W1559483280","https://openalex.org/W2082479932","https://openalex.org/W2932872266"],"abstract_inverted_index":{"Heterogeneous":[0],"Information":[1],"Network":[2],"(HIN)":[3],"embedding":[4,19],"has":[5],"been":[6],"a":[7,100,110,118],"prevalent":[8],"approach":[9],"to":[10,23,53,121,143,154,159,189],"learn":[11],"representations":[12],"off":[13],"semantically-rich":[14],"heterogeneous":[15,58,92],"networks.":[16],"Most":[17],"HIN":[18],"methods":[20,49],"exploit":[21],"meta-paths":[22,38],"retain":[24],"high-order":[25],"structures,":[26],"yet,":[27],"their":[28,40],"performance":[29],"is":[30,79],"conditioned":[31],"on":[32,141,171],"the":[33,36,43,66,84,128,134,183,190,196],"quality":[34],"of":[35,113,185],"(generated/manually-defined)":[37],"and":[39,102,115,175],"suitability":[41],"for":[42,99],"specific":[44],"label":[45],"set.":[46],"Whereas":[47],"other":[48,73],"adjust":[50],"random":[51,68,85,119,139],"walks":[52],"harness":[54],"or":[55,145],"skip":[56,155],"certain":[57,147],"structures":[59],"(e.g.":[60],"node":[61,168],"type(s)),":[62],"in":[63,193],"doing":[64],"so,":[65],"adjusted":[67],"walker":[69,86],"may":[70],"casually":[71],"omit":[72],"node/edge":[74],"types.":[75,149],"Our":[76],"key":[77],"insight":[78],"with":[80,195],"no":[81,89],"domain":[82],"knowledge,":[83],"should":[87],"hold":[88],"assumptions":[90],"about":[91],"structure":[93,192],"(i.e.":[94],"edge":[95,125,148],"types).":[96],"Thus,":[97],"aiming":[98],"flexible":[101],"general":[103],"method,":[104],"we":[105,132,151,165],"utilize":[106],"network":[107,129],"schema":[108],"as":[109],"unique":[111],"blueprint":[112],"HIN,":[114],"propose":[116],"SchemaWalk,":[117],"walk":[120],"uniformly":[122],"sample":[123],"all":[124],"types":[126],"within":[127],"schema.":[130],"Moreover,":[131],"identify":[133],"starvation":[135],"phenomenon":[136],"which":[137],"induces":[138],"walkers":[140],"HINs":[142],"under-":[144],"over-sample":[146],"Accordingly,":[150],"design":[152],"SchemaWalkHO":[153],"local":[156],"deficient":[157],"connectivity":[158],"preserve":[160],"uniform":[161],"sampling":[162],"distribution.":[163],"Finally,":[164],"carry":[166],"out":[167],"classification":[169],"experiments":[170],"four":[172],"real-world":[173],"HINs,":[174],"provide":[176],"in-depth":[177],"qualitative":[178],"analysis.":[179],"The":[180],"results":[181],"highlight":[182],"robustness":[184],"our":[186],"method":[187],"regardless":[188],"graph":[191],"contrast":[194],"state-of-the-art":[197],"baselines.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}