{"id":"https://openalex.org/W2809660921","doi":"https://doi.org/10.1145/3219819.3220062","title":"Hierarchical Taxonomy Aware Network Embedding","display_name":"Hierarchical Taxonomy Aware Network Embedding","publication_year":2018,"publication_date":"2018-07-19","ids":{"openalex":"https://openalex.org/W2809660921","doi":"https://doi.org/10.1145/3219819.3220062","mag":"2809660921"},"language":"en","primary_location":{"id":"doi:10.1145/3219819.3220062","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3219819.3220062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-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/A5101871710","display_name":"Jianxin Ma","orcid":"https://orcid.org/0009-0002-4614-5320"},"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":true,"raw_author_name":"Jianxin Ma","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009228005","display_name":"Peng Cui","orcid":"https://orcid.org/0000-0003-2957-8511"},"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":"Peng Cui","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101882666","display_name":"Xiao Wang","orcid":"https://orcid.org/0000-0002-3022-7260"},"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":"Xiao Wang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100339293","display_name":"Wenwu Zhu","orcid":"https://orcid.org/0000-0003-2236-9290"},"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":"Wenwu Zhu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101871710"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":5.7536,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.96780006,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1920","last_page":"1929"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9993000030517578,"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":0.9993000030517578,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.998199999332428,"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":0.9922999739646912,"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":0.6811419129371643},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6343475580215454},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5855464935302734},{"id":"https://openalex.org/keywords/taxonomy","display_name":"Taxonomy (biology)","score":0.5778343677520752},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5002586841583252},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.49932265281677246},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46249035000801086},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3964117765426636},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32891646027565}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6811419129371643},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6343475580215454},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5855464935302734},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.5778343677520752},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5002586841583252},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.49932265281677246},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46249035000801086},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3964117765426636},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32891646027565},{"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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3219819.3220062","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3219819.3220062","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W1888005072","https://openalex.org/W1967465043","https://openalex.org/W2001141328","https://openalex.org/W2005564522","https://openalex.org/W2046253692","https://openalex.org/W2053186076","https://openalex.org/W2062797058","https://openalex.org/W2090891622","https://openalex.org/W2124436241","https://openalex.org/W2132827946","https://openalex.org/W2144148101","https://openalex.org/W2150286230","https://openalex.org/W2151967501","https://openalex.org/W2154851992","https://openalex.org/W2156718197","https://openalex.org/W2158266063","https://openalex.org/W2162630660","https://openalex.org/W2242161203","https://openalex.org/W2387462954","https://openalex.org/W2393319904","https://openalex.org/W2402969480","https://openalex.org/W2415243320","https://openalex.org/W2577283662","https://openalex.org/W2579372251","https://openalex.org/W2604942799","https://openalex.org/W2607500032","https://openalex.org/W2622849676","https://openalex.org/W2623187518","https://openalex.org/W2624431344","https://openalex.org/W2743104969","https://openalex.org/W2768352146","https://openalex.org/W2788379054","https://openalex.org/W2795735740","https://openalex.org/W2917431650","https://openalex.org/W2950133940","https://openalex.org/W2950577311","https://openalex.org/W2962756421","https://openalex.org/W2963224980","https://openalex.org/W2963312446","https://openalex.org/W2963410212","https://openalex.org/W2963460103","https://openalex.org/W2963885834","https://openalex.org/W3103254545","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W4293651439"],"related_works":["https://openalex.org/W2931688134","https://openalex.org/W2377919138","https://openalex.org/W2378857091","https://openalex.org/W2166690231","https://openalex.org/W2950637221","https://openalex.org/W1497573972","https://openalex.org/W1846253165","https://openalex.org/W2932872266","https://openalex.org/W2071659383","https://openalex.org/W2124122503"],"abstract_inverted_index":{"Network":[0],"embedding":[1,103,130],"learns":[2],"the":[3,10,15,58,63,72,76,98,109,119,134,160,163,169,176,179,191,226],"low-dimensional":[4],"representations":[5,65,157,193],"for":[6,92,214],"vertices,":[7],"while":[8],"preserving":[9],"inter-vertex":[11],"similarity":[12,51],"reflected":[13],"by":[14,118],"network":[16,102,185],"structure.":[17],"The":[18,45,55,156,184,198],"neighborhood":[19],"structure":[20,186],"of":[21,47,52,57,69,145,153,158,178],"a":[22,105,128,141,195,204],"vertex":[23,142],"is":[24,111,116,187,201,212],"usually":[25],"closely":[26],"related":[27],"with":[28,36,151],"an":[29,89],"underlying":[30],"hierarchical":[31,73,99,182],"taxonomy---the":[32],"vertices":[33,81,161],"are":[34,149,165],"associated":[35,150],"successively":[37],"broader":[38],"categories":[39,46,152,164],"that":[40,62,132,148,219],"can":[41],"be":[42],"organized":[43],"hierarchically.":[44],"different":[48,53,154],"levels":[49,68],"reflects":[50],"granularity.":[54,70,155],"hierarchy":[56],"taxonomy":[59,74,100,110],"therefore":[60],"requires":[61],"learned":[64],"support":[66],"multiple":[67,146],"Moreover,":[71],"enables":[75],"information":[77],"to":[78,174],"flow":[79],"between":[80],"via":[82,194],"their":[83],"common":[84],"categories,":[85],"and":[86,114,162],"thus":[87],"provides":[88],"effective":[90],"mechanism":[91],"alleviating":[93],"data":[94],"scarcity.":[95],"However,":[96],"incorporating":[97],"into":[101],"poses":[104],"great":[106],"challenge":[107],"(since":[108],"generally":[112],"unknown),":[113],"it":[115],"neglected":[117],"existing":[120],"approaches.":[121],"In":[122,138],"this":[123],"paper,":[124],"we":[125],"propose":[126],"NetHiex,":[127],"NETwork":[129],"model":[131,200],"captures":[133],"latent":[135,192],"HIErarchical":[136],"taXonomy.":[137],"our":[139],"model,":[140],"representation":[143],"consists":[144],"components":[147],"both":[159],"co-regularized.":[166],"We":[167],"employ":[168],"nested":[170],"Chinese":[171],"restaurant":[172],"process":[173],"guide":[175],"search":[177],"most":[180],"plausible":[181],"taxonomy.":[183],"then":[188],"recovered":[189],"from":[190],"Bernoulli":[196],"distribution.":[197],"whole":[199],"unified":[202],"within":[203],"nonparametric":[205],"probabilistic":[206],"framework.":[207],"A":[208],"scalable":[209],"expectation-maximization":[210],"algorithm":[211],"derived":[213],"optimization.":[215],"Empirical":[216],"results":[217],"demonstrate":[218],"NetHiex":[220],"achieves":[221],"significant":[222],"performance":[223],"gain":[224],"over":[225],"state-of-arts.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":11},{"year":2018,"cited_by_count":1}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}
