{"id":"https://openalex.org/W4321613242","doi":"https://doi.org/10.1007/s10115-023-01836-3","title":"Density of states for fast embedding node-attributed graphs","display_name":"Density of states for fast embedding node-attributed graphs","publication_year":2023,"publication_date":"2023-02-20","ids":{"openalex":"https://openalex.org/W4321613242","doi":"https://doi.org/10.1007/s10115-023-01836-3"},"language":"en","primary_location":{"id":"doi:10.1007/s10115-023-01836-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10115-023-01836-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10115-023-01836-3.pdf","source":{"id":"https://openalex.org/S81770430","display_name":"Knowledge and Information Systems","issn_l":"0219-1377","issn":["0219-1377","0219-3116"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knowledge and Information Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10115-023-01836-3.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101697859","display_name":"Lingxiao Zhao","orcid":"https://orcid.org/0000-0003-2553-3148"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lingxiao Zhao","raw_affiliation_strings":["Heinz College, Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Heinz College, Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074374806","display_name":"Saurabh Sawlani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saurabh Sawlani","raw_affiliation_strings":["SoundHound Inc., Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"SoundHound Inc., Berlin, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001634795","display_name":"Leman Akoglu","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leman Akoglu","raw_affiliation_strings":["Heinz College, Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Heinz College, Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101697859"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":{"value":2290,"currency":"EUR","value_usd":2890},"apc_paid":{"value":2290,"currency":"EUR","value_usd":2890},"fwci":0.3266,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.51154954,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"65","issue":"6","first_page":"2455","last_page":"2483"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994999766349792,"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"}},{"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9779999852180481,"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":0.7681231498718262},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7476722598075867},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6139072179794312},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.607097327709198},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5909901261329651},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5598388314247131},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.445625901222229},{"id":"https://openalex.org/keywords/topological-graph-theory","display_name":"Topological graph theory","score":0.41294416785240173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.319105863571167},{"id":"https://openalex.org/keywords/pathwidth","display_name":"Pathwidth","score":0.2034391164779663},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.18494340777397156}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7681231498718262},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7476722598075867},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6139072179794312},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.607097327709198},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5909901261329651},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5598388314247131},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.445625901222229},{"id":"https://openalex.org/C157406716","wikidata":"https://www.wikidata.org/wiki/Q4115842","display_name":"Topological graph theory","level":5,"score":0.41294416785240173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.319105863571167},{"id":"https://openalex.org/C43517604","wikidata":"https://www.wikidata.org/wiki/Q7144893","display_name":"Pathwidth","level":4,"score":0.2034391164779663},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.18494340777397156},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10115-023-01836-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10115-023-01836-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10115-023-01836-3.pdf","source":{"id":"https://openalex.org/S81770430","display_name":"Knowledge and Information Systems","issn_l":"0219-1377","issn":["0219-1377","0219-3116"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knowledge and Information Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10115-023-01836-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10115-023-01836-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10115-023-01836-3.pdf","source":{"id":"https://openalex.org/S81770430","display_name":"Knowledge and Information Systems","issn_l":"0219-1377","issn":["0219-1377","0219-3116"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knowledge and Information Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1171700966","display_name":null,"funder_award_id":"NSF CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5512748762","display_name":null,"funder_award_id":"1452425","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","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"},{"id":"https://openalex.org/F4320310207","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4321613242.pdf"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1179283095","https://openalex.org/W1492230849","https://openalex.org/W1595344052","https://openalex.org/W1997120876","https://openalex.org/W2003582562","https://openalex.org/W2006340475","https://openalex.org/W2008857988","https://openalex.org/W2020328455","https://openalex.org/W2034618876","https://openalex.org/W2069092471","https://openalex.org/W2071718780","https://openalex.org/W2075984423","https://openalex.org/W2081812591","https://openalex.org/W2091082715","https://openalex.org/W2092750499","https://openalex.org/W2099438806","https://openalex.org/W2112494680","https://openalex.org/W2114030927","https://openalex.org/W2150256170","https://openalex.org/W2754038670","https://openalex.org/W2804381853","https://openalex.org/W2914721378","https://openalex.org/W2925177113","https://openalex.org/W2946144513","https://openalex.org/W2948246283","https://openalex.org/W2963084622","https://openalex.org/W3005187920","https://openalex.org/W3080335912","https://openalex.org/W3103523530","https://openalex.org/W3103868164","https://openalex.org/W3106039696","https://openalex.org/W3187220393","https://openalex.org/W3196790714","https://openalex.org/W4242825868","https://openalex.org/W6633728996"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2123605750","https://openalex.org/W2912814903","https://openalex.org/W2088740331","https://openalex.org/W2950907416","https://openalex.org/W3038102983","https://openalex.org/W2082479932","https://openalex.org/W2932872266","https://openalex.org/W3132395223"],"abstract_inverted_index":{"Abstract":[0],"Given":[1],"a":[2,50,99],"node-attributed":[3],"graph,":[4,100],"how":[5],"can":[6,102],"we":[7,67,158],"efficiently":[8],"represent":[9],"it":[10,58,81,113,130,138,146,175],"with":[11,184],"few":[12],"numerical":[13],"features":[14],"that":[15,66],"expressively":[16],"reflect":[17],"its":[18],"topology":[19],"and":[20,74,83,89,105,122,162,170,180,195],"attribute":[21,106],"information?":[22],"We":[23],"propose":[24],"A-DOGE":[25,45,101,165],",":[26],"for":[27,64,76,85,132,148],"attributed":[28,87],"DOS-based":[29],"graph":[30,90,134,141,168,171],"embedding,":[31],"based":[32,93],"on":[33,60,94,166],"density":[34],"of":[35,53,98,143,164],"states":[36],"(DOS,":[37],"a.k.a.":[38],"spectral":[39],"density)":[40],"to":[41,48,69,118,125],"tackle":[42],"this":[43],"problem.":[44],"is":[46,114],"designed":[47],"fulfill":[49],"long":[51],"desiderata":[52],"desirable":[54],"characteristics.":[55],"Most":[56],"notably,":[57],"capitalizes":[59],"efficient":[61],"approximation":[62],"algorithms":[63],"DOS,":[65],"extend":[68],"blend":[70],"in":[71],"node":[72],"labels":[73],"attributes":[75],"the":[77,95,160,190],"first":[78],"time,":[79],"making":[80,145],"fast":[82],"scalable":[84],"large":[86],"graphs":[88],"databases.":[91],"Being":[92],"entire":[96],"eigenspectrum":[97],"capture":[103],"structural":[104],"properties":[107],"at":[108],"multiple":[109],"(\u201cglocal\u201d)":[110],"scales.":[111],"Moreover,":[112],"unsupervised":[115,178],"(i.e.,":[116],"agnostic":[117],"any":[119],"specific":[120],"objective)":[121],"lends":[123],"itself":[124],"various":[126],"interpretations,":[127],"which":[128],"makes":[129],"suitable":[131],"exploratory":[133,167],"mining":[135],"tasks.":[136],"Finally,":[137],"processes":[139],"each":[140],"independent":[142],"others,":[144],"amenable":[147],"streaming":[149],"settings":[150],"as":[151,153],"well":[152],"parallelization.":[154],"Through":[155],"extensive":[156],"experiments,":[157],"show":[159],"efficacy":[161],"efficiency":[163],"analysis":[169],"classification":[172],"tasks,":[173],"where":[174],"significantly":[176],"outperforms":[177],"baselines":[179],"achieves":[181],"competitive":[182],"performance":[183],"modern":[185],"supervised":[186],"GNNs,":[187],"while":[188],"achieving":[189],"best":[191],"trade-off":[192],"between":[193],"accuracy":[194],"runtime.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}