{"id":"https://openalex.org/W2952720493","doi":"https://doi.org/10.1145/3292500.3330918","title":"Scalable Global Alignment Graph Kernel Using Random Features","display_name":"Scalable Global Alignment Graph Kernel Using Random Features","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2952720493","doi":"https://doi.org/10.1145/3292500.3330918","mag":"2952720493"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330918","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th 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/A5101478122","display_name":"Lingfei Wu","orcid":"https://orcid.org/0009-0008-8081-6275"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lingfei Wu","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073803011","display_name":"Ian En-Hsu Yen","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":"Ian En-Hsu Yen","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103037437","display_name":"Zhen Zhang","orcid":"https://orcid.org/0000-0002-6006-7157"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhen Zhang","raw_affiliation_strings":["Washington University in St. Louis, St. Louis, IN, USA"],"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis, St. Louis, IN, USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043893150","display_name":"Kun Xu","orcid":"https://orcid.org/0000-0002-1663-9998"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kun Xu","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048756500","display_name":"Liang Zhao","orcid":"https://orcid.org/0000-0002-2648-9989"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liang Zhao","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022800038","display_name":"Xi Peng","orcid":"https://orcid.org/0000-0002-5727-2790"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xi Peng","raw_affiliation_strings":["University of Delaware, Newark, DE, USA"],"affiliations":[{"raw_affiliation_string":"University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052933431","display_name":"Yinglong Xia","orcid":"https://orcid.org/0000-0002-8155-5440"},"institutions":[{"id":"https://openalex.org/I4210146936","display_name":"Huawei Technologies (United States)","ror":"https://ror.org/03jyqk712","country_code":"US","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210146936"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yinglong Xia","raw_affiliation_strings":["Huawei, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Huawei, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210146936"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028089542","display_name":"Char\u0173 C. Aggarwal","orcid":"https://orcid.org/0000-0003-2579-7581"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charu Aggarwal","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5101478122"],"corresponding_institution_ids":["https://openalex.org/I1341412227"],"apc_list":null,"apc_paid":null,"fwci":2.9403,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.93027499,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1418","last_page":"1428"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9919999837875366,"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":0.9858999848365784,"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/graph-kernel","display_name":"Graph kernel","score":0.7489339113235474},{"id":"https://openalex.org/keywords/random-graph","display_name":"Random graph","score":0.5072038173675537},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48568543791770935},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.43389901518821716},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.4221709966659546},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4020887613296509},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3752218782901764},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34196653962135315},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.3223726451396942},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.32067960500717163},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.23454517126083374},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.23061591386795044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1792026162147522},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.10560277104377747}],"concepts":[{"id":"https://openalex.org/C100595998","wikidata":"https://www.wikidata.org/wiki/Q11731931","display_name":"Graph kernel","level":5,"score":0.7489339113235474},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.5072038173675537},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48568543791770935},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.43389901518821716},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.4221709966659546},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4020887613296509},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3752218782901764},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34196653962135315},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.3223726451396942},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.32067960500717163},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.23454517126083374},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.23061591386795044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1792026162147522},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.10560277104377747}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330918","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W658020064","https://openalex.org/W1576213419","https://openalex.org/W1816257748","https://openalex.org/W1975675300","https://openalex.org/W1992208818","https://openalex.org/W2008857988","https://openalex.org/W2013039900","https://openalex.org/W2013698884","https://openalex.org/W2024364296","https://openalex.org/W2027329917","https://openalex.org/W2039444222","https://openalex.org/W2065745355","https://openalex.org/W2078289947","https://openalex.org/W2081301924","https://openalex.org/W2107410045","https://openalex.org/W2118563516","https://openalex.org/W2118585731","https://openalex.org/W2132914434","https://openalex.org/W2142498761","https://openalex.org/W2143668817","https://openalex.org/W2144902422","https://openalex.org/W2147286743","https://openalex.org/W2159156271","https://openalex.org/W2184466087","https://openalex.org/W2224709232","https://openalex.org/W2311954821","https://openalex.org/W2328725012","https://openalex.org/W2393444374","https://openalex.org/W2406128552","https://openalex.org/W2465015709","https://openalex.org/W2492608700","https://openalex.org/W2549898883","https://openalex.org/W2604795503","https://openalex.org/W2666296020","https://openalex.org/W2760503384","https://openalex.org/W2766218623","https://openalex.org/W2773088683","https://openalex.org/W2803475843","https://openalex.org/W2911738047","https://openalex.org/W2913825337","https://openalex.org/W2950351882","https://openalex.org/W2950656243","https://openalex.org/W2952419077","https://openalex.org/W2963013450","https://openalex.org/W2963169996","https://openalex.org/W2963655370","https://openalex.org/W2963709899","https://openalex.org/W3138558418","https://openalex.org/W4387560735","https://openalex.org/W6677656871","https://openalex.org/W6681029592"],"related_works":["https://openalex.org/W2082971831","https://openalex.org/W2551512322","https://openalex.org/W1983263273","https://openalex.org/W2828181497","https://openalex.org/W618655101","https://openalex.org/W3100948281","https://openalex.org/W3006070568","https://openalex.org/W2294970809","https://openalex.org/W2179275589","https://openalex.org/W2096302783"],"abstract_inverted_index":{"Graph":[0],"kernels":[1,57],"are":[2,58],"widely":[3],"used":[4],"for":[5],"measuring":[6],"the":[7,43,65,79,82,85,104,129,182,185,188],"similarity":[8],"between":[9],"graphs.":[10,35,86,189],"Many":[11],"existing":[12,126],"graph":[13,39,56,66,98,135,160,167,205],"kernels,":[14,40,99,128],"which":[15,41,100,146,162],"focus":[16],"on":[17,120,193],"local":[18],"patterns":[19],"within":[20],"graphs":[21,108],"rather":[22],"than":[23],"their":[24],"global":[25,38,96,105,127],"properties,":[26],"suffer":[27],"from":[28],"significant":[29],"structure":[30],"information":[31],"loss":[32],"when":[33],"representing":[34],"Some":[36],"recent":[37],"utilizes":[42],"alignment":[44,97],"of":[45,49,78,84,95,107,187],"geometric":[46,111],"node":[47,112,117],"embeddings":[48,113],"graphs,":[50,145],"yield":[51,149],"state-of-the-art":[52,204],"performance.":[53],"However,":[54],"these":[55],"not":[59],"necessarily":[60],"positive-definite.":[61,133],"More":[62],"importantly,":[63],"computing":[64],"kernel":[67,131,136],"matrix":[68],"will":[69],"have":[70],"at":[71],"least":[72],"quadratic":[73],"time":[74],"complexity":[75],"in":[76],"terms":[77],"number":[80,183],"and":[81,114,184],"size":[83,186],"In":[87,170],"this":[88],"paper,":[89],"we":[90],"propose":[91],"a":[92,141],"new":[93],"family":[94],"take":[101],"into":[102],"account":[103],"properties":[106],"by":[109,139],"using":[110],"an":[115],"associated":[116],"transportation":[118],"based":[119],"earth":[121],"mover's":[122],"distance.":[123],"Compared":[124],"to":[125,158,175,181],"proposed":[130],"is":[132,137,163,173],"Our":[134],"obtained":[138],"defining":[140],"distribution":[142],"over":[143],"random":[144,150,154],"can":[147],"naturally":[148],"feature":[151,155],"approximations.":[152],"The":[153,190],"approximations":[156],"lead":[157],"our":[159],"embeddings,":[161],"named":[164],"as":[165],"\"random":[166],"embeddings\"":[168],"(RGE).":[169],"particular,":[171],"RGE":[172,199],"shown":[174],"achieve":[176],"(quasi-)linear":[177],"scalability":[178],"with":[179],"respect":[180],"experimental":[191],"results":[192],"nine":[194],"benchmark":[195],"datasets":[196],"demonstrate":[197],"that":[198],"outperforms":[200],"or":[201],"matches":[202],"twelve":[203],"classification":[206],"algorithms.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
