{"id":"https://openalex.org/W4391094282","doi":"https://doi.org/10.1109/access.2024.3356572","title":"A Graph Classification Method Based on Support Vector Machines and Locality-Sensitive Hashing","display_name":"A Graph Classification Method Based on Support Vector Machines and Locality-Sensitive Hashing","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391094282","doi":"https://doi.org/10.1109/access.2024.3356572"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3356572","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3356572","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10410853.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10410853.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046605569","display_name":"Mar\u00eda D. Gonz\u00e1lez-Lima","orcid":"https://orcid.org/0000-0002-7750-9564"},"institutions":[{"id":"https://openalex.org/I4210112678","display_name":"University of the Coast","ror":"https://ror.org/01v5nhr20","country_code":"CO","type":"education","lineage":["https://openalex.org/I4210112678"]}],"countries":["CO"],"is_corresponding":true,"raw_author_name":"Mar\u00eda D. Gonzalez-Lima","raw_affiliation_strings":["Department of Mathematics and Statistics, University of the North, Barranquilla, Colombia","Department of Mathematics and Statistics, University of the North, Barranquilla, COLOMBIA"],"raw_orcid":"https://orcid.org/0000-0002-7750-9564","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, University of the North, Barranquilla, Colombia","institution_ids":["https://openalex.org/I4210112678"]},{"raw_affiliation_string":"Department of Mathematics and Statistics, University of the North, Barranquilla, COLOMBIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078859947","display_name":"Carenne C. Lude\u00f1a","orcid":"https://orcid.org/0000-0002-0167-9188"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carenne C. Lude\u00f1a","raw_affiliation_strings":["Matrix CPM Solutions, Bogot&#x00E1;, Colombia"],"raw_orcid":"https://orcid.org/0000-0002-0167-9188","affiliations":[{"raw_affiliation_string":"Matrix CPM Solutions, Bogot&#x00E1;, Colombia","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5093760313","display_name":"Gibran G. Otazo-Sanchez","orcid":"https://orcid.org/0000-0002-6797-7698"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gibran G. Otazo-Sanchez","raw_affiliation_strings":["AI Factory, BBVA, M&#x00E9;xico City, M&#x00E9;xico"],"raw_orcid":"https://orcid.org/0000-0002-6797-7698","affiliations":[{"raw_affiliation_string":"AI Factory, BBVA, M&#x00E9;xico City, M&#x00E9;xico","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046605569"],"corresponding_institution_ids":["https://openalex.org/I4210112678"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.3254,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59940454,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"12","issue":null,"first_page":"15791","last_page":"15799"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991000294685364,"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":0.9991000294685364,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9919000267982483,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/support-vector-machine","display_name":"Support vector machine","score":0.6392191648483276},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.58971107006073},{"id":"https://openalex.org/keywords/locality-sensitive-hashing","display_name":"Locality-sensitive hashing","score":0.5373725891113281},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4683726727962494},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44961681962013245},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.426943302154541},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4134429395198822},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4112907350063324},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.21754509210586548}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6392191648483276},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.58971107006073},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.5373725891113281},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4683726727962494},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44961681962013245},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.426943302154541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4134429395198822},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4112907350063324},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.21754509210586548},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3356572","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3356572","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10410853.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:1398d2d12fda4258b6ecba3f4e891a13","is_oa":true,"landing_page_url":"https://doaj.org/article/1398d2d12fda4258b6ecba3f4e891a13","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 15791-15799 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3356572","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3356572","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10410853.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391094282.pdf","grobid_xml":"https://content.openalex.org/works/W4391094282.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1563088657","https://openalex.org/W1664627118","https://openalex.org/W1816257748","https://openalex.org/W1986630010","https://openalex.org/W2008620264","https://openalex.org/W2047542122","https://openalex.org/W2086074297","https://openalex.org/W2136824894","https://openalex.org/W2147717514","https://openalex.org/W2162006472","https://openalex.org/W2164641162","https://openalex.org/W2309460698","https://openalex.org/W2407879741","https://openalex.org/W2485160283","https://openalex.org/W2782485997","https://openalex.org/W2925177113","https://openalex.org/W2998490552","https://openalex.org/W3010957828","https://openalex.org/W3028283557","https://openalex.org/W3044028388","https://openalex.org/W3093943311","https://openalex.org/W3095213891","https://openalex.org/W3103254793","https://openalex.org/W3103523530","https://openalex.org/W4221098058","https://openalex.org/W4247914182","https://openalex.org/W4281479327","https://openalex.org/W4306152347","https://openalex.org/W6683984541","https://openalex.org/W6752012279","https://openalex.org/W6847388455"],"related_works":["https://openalex.org/W4287326768","https://openalex.org/W2393322642","https://openalex.org/W3131198547","https://openalex.org/W3099654136","https://openalex.org/W605817175","https://openalex.org/W3161963277","https://openalex.org/W2144265691","https://openalex.org/W2033383639","https://openalex.org/W3108918257","https://openalex.org/W3016124764"],"abstract_inverted_index":{"Graphs":[0],"classification":[1,116,183,223],"is":[2,30,59,121],"a":[3,31,77,82,106,205],"relevant":[4],"problem":[5,68,101],"that":[6,93,140],"arises":[7],"in":[8,105],"many":[9],"disciplines.":[10],"Using":[11],"graphs":[12],"directly":[13],"instead":[14],"of":[15,23,39,48,51,56,62,80,86,97,108,126,148,212,215],"vectorization":[16],"allows":[17],"to":[18,70],"exploit":[19],"the":[20,24,37,49,54,60,66,87,98,109,115,124,132,144,149,178,182,186,195,199,213],"intrinsic":[21],"representations":[22],"data.":[25],"Support":[26],"Vector":[27],"Machines":[28],"(SVM)":[29],"supervised":[32],"learning":[33],"method":[34,78,120],"based":[35,122],"on":[36,123,165],"use":[38,125],"graph":[40,89,138,158,222],"kernel":[41],"functions":[42],"used":[43],"for":[44,129,218],"this":[45,73],"task.":[46],"One":[47],"problems":[50,224],"SVM,":[52],"as":[53,151,153],"number":[55],"samples":[57],"increases,":[58],"cost":[61],"storing":[63],"and":[64,111,162,167,181,194],"solving":[65,219],"optimization":[67,100],"related":[69],"SVM.":[71,227],"In":[72],"work":[74],"we":[75],"propose":[76],"capable":[79],"finding":[81],"small":[83],"representative":[84],"subset":[85],"whole":[88],"data":[90,170,201],"set":[91,202],"such":[92],"an":[94],"approximate":[95],"solution":[96],"SVM":[99,187,196],"can":[102],"be":[103],"obtained":[104,188,197],"fraction":[107],"time,":[110],"without":[112],"significantly":[113],"degrading":[114],"prediction":[117],"error.":[118],"The":[119,174,208],"Locality-Sensitive":[127],"Hashing":[128],"projecting":[130],"over":[131,198,204],"Hilbert":[133],"spaces":[134],"defined":[135],"by":[136],"appropriate":[137],"kernels":[139,159],"measure":[141],"similarity":[142],"between":[143,185],"graphs.":[145],"A":[146],"description":[147],"algorithm,":[150],"well":[152],"numerical":[154,175],"results":[155,209],"using":[156,226],"two":[157],"(Simple":[160],"Product":[161],"Random":[163],"Walk)":[164],"simulated":[166],"real":[168],"life":[169],"sets":[171],"are":[172],"presented.":[173],"experiments":[176],"compare":[177],"training":[179],"times":[180],"error":[184],"with":[189],"our":[190,216],"smart":[191],"sampling":[192],"approach,":[193],"complete":[200],"or":[203],"random":[206],"sub-sample.":[207],"offer":[210],"evidence":[211],"advantages":[214],"proposal":[217],"large":[220],"scale":[221],"when":[225]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
