{"id":"https://openalex.org/W2788614083","doi":"https://doi.org/10.1145/3178876.3186120","title":"VERSE","display_name":"VERSE","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2788614083","doi":"https://doi.org/10.1145/3178876.3186120","mag":"2788614083"},"language":"en","primary_location":{"id":"doi:10.1145/3178876.3186120","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186120","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3186120&type=pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=3186120&type=pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032806916","display_name":"Anton Tsitsulin","orcid":"https://orcid.org/0000-0001-5519-7961"},"institutions":[{"id":"https://openalex.org/I143288331","display_name":"Hasso Plattner Institute","ror":"https://ror.org/058rn5r42","country_code":"DE","type":"facility","lineage":["https://openalex.org/I143288331","https://openalex.org/I176453806"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Anton Tsitsulin","raw_affiliation_strings":["Hasso Plattner Institute, Potsdam, Germany"],"affiliations":[{"raw_affiliation_string":"Hasso Plattner Institute, Potsdam, Germany","institution_ids":["https://openalex.org/I143288331"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009276458","display_name":"Davide Mottin","orcid":"https://orcid.org/0000-0001-8256-2258"},"institutions":[{"id":"https://openalex.org/I143288331","display_name":"Hasso Plattner Institute","ror":"https://ror.org/058rn5r42","country_code":"DE","type":"facility","lineage":["https://openalex.org/I143288331","https://openalex.org/I176453806"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Davide Mottin","raw_affiliation_strings":["Hasso Plattner Institute, Potsdam, Germany"],"affiliations":[{"raw_affiliation_string":"Hasso Plattner Institute, Potsdam, Germany","institution_ids":["https://openalex.org/I143288331"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103092057","display_name":"Panagiotis Karras","orcid":"https://orcid.org/0000-0003-0509-9129"},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Panagiotis Karras","raw_affiliation_strings":["Aarhus University, Aarhus, Denmark"],"affiliations":[{"raw_affiliation_string":"Aarhus University, Aarhus, Denmark","institution_ids":["https://openalex.org/I204337017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053906729","display_name":"Emmanuel M\u00fcller","orcid":"https://orcid.org/0000-0002-5409-6875"},"institutions":[{"id":"https://openalex.org/I143288331","display_name":"Hasso Plattner Institute","ror":"https://ror.org/058rn5r42","country_code":"DE","type":"facility","lineage":["https://openalex.org/I143288331","https://openalex.org/I176453806"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Emmanuel M\u00fcller","raw_affiliation_strings":["Hasso Plattner Institute, Potsdam, Germany"],"affiliations":[{"raw_affiliation_string":"Hasso Plattner Institute, Potsdam, Germany","institution_ids":["https://openalex.org/I143288331"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5032806916"],"corresponding_institution_ids":["https://openalex.org/I143288331"],"apc_list":null,"apc_paid":null,"fwci":26.2293,"has_fulltext":true,"cited_by_count":268,"citation_normalized_percentile":{"value":0.99574457,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"539","last_page":"548"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9995999932289124,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9922999739646912,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7453925609588623},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5464474558830261},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5441603660583496},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5288753509521484},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.49483174085617065},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.47923973202705383},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.45814698934555054},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4549500644207001},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.4308962821960449},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4248695969581604},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41072097420692444},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4082260727882385},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3301423192024231},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.0868973433971405}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7453925609588623},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5464474558830261},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5441603660583496},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5288753509521484},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.49483174085617065},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.47923973202705383},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.45814698934555054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4549500644207001},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.4308962821960449},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4248695969581604},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41072097420692444},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4082260727882385},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3301423192024231},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0868973433971405},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3178876.3186120","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186120","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3186120&type=pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1803.04742","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1803.04742","pdf_url":"https://arxiv.org/pdf/1803.04742","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:pure.atira.dk:openaire/5ea76c7a-c724-43a4-a6d6-2cdb9725e596","is_oa":true,"landing_page_url":"https://pure.au.dk/portal/en/publications/5ea76c7a-c724-43a4-a6d6-2cdb9725e596","pdf_url":"https://pure.au.dk/ws/files/135402344/p539_tsitsulin.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Tsitsulin, A, Mottin, D, Karras, P & M\u00fcller, E 2018, VERSE: Versatile Graph Embeddings from Similarity Measures. in P-A Champin, F Gandon & L M\u00e9dini (eds), The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018 : WWW '18. Association for Computing Machinery, pp. 539-548, The Web Conference, Lyon, France, 23/04/2018. https://doi.org/10.1145/3178876.3186120","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1145/3178876.3186120","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186120","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3186120&type=pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2788614083.pdf","grobid_xml":"https://content.openalex.org/works/W2788614083.grobid-xml"},"referenced_works_count":65,"referenced_works":["https://openalex.org/W1492581097","https://openalex.org/W1742512077","https://openalex.org/W1854214752","https://openalex.org/W1888005072","https://openalex.org/W1979104937","https://openalex.org/W2001141328","https://openalex.org/W2001325956","https://openalex.org/W2003707464","https://openalex.org/W2020652789","https://openalex.org/W2022704179","https://openalex.org/W2026417691","https://openalex.org/W2046253692","https://openalex.org/W2053186076","https://openalex.org/W2066636486","https://openalex.org/W2073459066","https://openalex.org/W2090891622","https://openalex.org/W2105543219","https://openalex.org/W2112090702","https://openalex.org/W2112429379","https://openalex.org/W2116819994","https://openalex.org/W2117831564","https://openalex.org/W2118585731","https://openalex.org/W2120861206","https://openalex.org/W2131681506","https://openalex.org/W2138204974","https://openalex.org/W2140679639","https://openalex.org/W2142535891","https://openalex.org/W2146241755","https://openalex.org/W2151936673","https://openalex.org/W2152790380","https://openalex.org/W2154851992","https://openalex.org/W2156718197","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2187089797","https://openalex.org/W2250539671","https://openalex.org/W2387462954","https://openalex.org/W2393319904","https://openalex.org/W2408567027","https://openalex.org/W2415243320","https://openalex.org/W2468480579","https://openalex.org/W2557283755","https://openalex.org/W2579372251","https://openalex.org/W2583803680","https://openalex.org/W2607500032","https://openalex.org/W2614812929","https://openalex.org/W2618530766","https://openalex.org/W2619010033","https://openalex.org/W2739946816","https://openalex.org/W2740296442","https://openalex.org/W2767304771","https://openalex.org/W2767500585","https://openalex.org/W2950133940","https://openalex.org/W2950797609","https://openalex.org/W2962756421","https://openalex.org/W3099768174","https://openalex.org/W3101413764","https://openalex.org/W3101444938","https://openalex.org/W3104097132","https://openalex.org/W3104211877","https://openalex.org/W3105705953","https://openalex.org/W3148981562","https://openalex.org/W4285719527","https://openalex.org/W4294170691","https://openalex.org/W4297571622"],"related_works":["https://openalex.org/W2319693127","https://openalex.org/W308539617","https://openalex.org/W2072263576","https://openalex.org/W2474567666","https://openalex.org/W1940044583","https://openalex.org/W3206528106","https://openalex.org/W3036264823","https://openalex.org/W2806903871","https://openalex.org/W2912814903","https://openalex.org/W2123605750"],"abstract_inverted_index":{"Embedding":[0],"a":[1,6,38,81,98,109,127],"web-scale":[2],"information":[3,132],"network":[4],"into":[5],"low-dimensional":[7],"vector":[8],"space":[9,172],"facilitates":[10],"tasks":[11,165],"such":[12,27,105],"as":[13,44,183],"link":[14],"prediction,":[15],"classification,":[16],"and":[17,84,141,159,166,171],"visualization.":[18],"Past":[19],"research":[20],"has":[21],"addressed":[22],"the":[23,47,65,95,130,175,184],"problem":[24],"of":[25,68,97,157],"extracting":[26],"embeddings":[28,90,106],"by":[29,107],"adopting":[30],"methods":[31,154],"from":[32],"words":[33],"to":[34,71,93],"graphs,":[35],"without":[36],"defining":[37],"clearly":[39],"comprehensible":[40],"graph-related":[41],"objective.":[42],"Yet,":[43],"we":[45,63,75,124],"show,":[46],"objectives":[48],"used":[49],"in":[50,155,161,169],"past":[51],"works":[52,70],"implicitly":[53],"utilize":[54],"similarity":[55,66,101,122,150],"measures":[56],"among":[57],"graph":[58,89],"nodes.":[59],"In":[60],"this":[61],"paper,":[62],"carry":[64],"orientation":[67],"previous":[69],"its":[72,114],"logical":[73],"conclusion;":[74],"propose":[76],"VERtex":[77],"Similarity":[78],"Embeddings":[79],"(VERSE),":[80],"simple,":[82],"versatile,":[83],"memory-efficient":[85],"method":[86],"that":[87,145],"derives":[88],"explicitly":[91],"calibrated":[92],"preserve":[94],"distributions":[96],"selected":[99],"vertex-to-vertex":[100],"measure.":[102],"VERSE":[103],"learns":[104],"training":[108],"single-layer":[110],"neural":[111],"network.":[112],"While":[113],"default,":[115],"scalable":[116,176],"version":[117],"does":[118],"so":[119],"via":[120],"sampling":[121],"information,":[123],"also":[125],"develop":[126],"variant":[128,178],"using":[129],"full":[131,186],"per":[133],"vertex.":[134],"Our":[135],"experimental":[136],"study":[137],"on":[138],"standard":[139],"benchmarks":[140],"real-world":[142],"datasets":[143],"demonstrates":[144],"VERSE,":[146],"instantiated":[147],"with":[148],"diverse":[149],"measures,":[151],"outperforms":[152],"state-of-the-art":[153],"terms":[156],"precision":[158],"recall":[160],"major":[162],"data":[163],"mining":[164],"supersedes":[167],"them":[168],"time":[170],"efficiency,":[173],"while":[174],"sampling-based":[177],"achieves":[179],"equally":[180],"good":[181],"result":[182],"non-scalable":[185],"variant.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":29},{"year":2022,"cited_by_count":38},{"year":2021,"cited_by_count":54},{"year":2020,"cited_by_count":51},{"year":2019,"cited_by_count":41},{"year":2018,"cited_by_count":9}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2018-03-06T00:00:00"}