{"id":"https://openalex.org/W4386815495","doi":"https://doi.org/10.14428/esann/2023.es2023-32","title":"Graph-based Categorical Embedding","display_name":"Graph-based Categorical Embedding","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386815495","doi":"https://doi.org/10.14428/esann/2023.es2023-32"},"language":"en","primary_location":{"id":"doi:10.14428/esann/2023.es2023-32","doi":"https://doi.org/10.14428/esann/2023.es2023-32","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2023.es2023-32","pdf_url":"https://doi.org/10.14428/esann/2023.es2023-32","source":{"id":"https://openalex.org/S7407087818","display_name":"European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2023 proceesdings","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.14428/esann/2023.es2023-32","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103325594","display_name":"Weiwei Wang","orcid":"https://orcid.org/0000-0002-6970-3789","observed_orcids":["https://orcid.org/0000-0002-6970-3789"]},"institutions":[{"id":"https://openalex.org/I34352273","display_name":"Maastricht University","ror":"https://ror.org/02jz4aj89","country_code":"NL","type":"education","lineage":["https://openalex.org/I34352273"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Weiwei Wang","raw_affiliation_strings":["-Maastricht University -Institute of Data Science Paul-Henri Spaaklaan 1, 6229 EN Maastricht -The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"-Maastricht University -Institute of Data Science Paul-Henri Spaaklaan 1, 6229 EN Maastricht -The Netherlands","institution_ids":["https://openalex.org/I34352273"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060319953","display_name":"Stefano Bromuri","orcid":"https://orcid.org/0000-0001-6205-8679","observed_orcids":["https://orcid.org/0000-0001-6205-8679"]},"institutions":[{"id":"https://openalex.org/I7876267","display_name":"Open University of the Netherlands","ror":"https://ror.org/018dfmf50","country_code":"NL","type":"education","lineage":["https://openalex.org/I7876267"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Stefano Bromuri","raw_affiliation_strings":["-Open University -Computer Science Department Valkenburgerweg 177, 6419 AT Heerlen -The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"-Open University -Computer Science Department Valkenburgerweg 177, 6419 AT Heerlen -The Netherlands","institution_ids":["https://openalex.org/I7876267"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044836472","display_name":"Michel Dumontier","orcid":"https://orcid.org/0000-0003-4727-9435","observed_orcids":["https://orcid.org/0000-0003-4727-9435"]},"institutions":[{"id":"https://openalex.org/I34352273","display_name":"Maastricht University","ror":"https://ror.org/02jz4aj89","country_code":"NL","type":"education","lineage":["https://openalex.org/I34352273"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Michel Dumontier","raw_affiliation_strings":["-Maastricht University -Institute of Data Science Paul-Henri Spaaklaan 1, 6229 EN Maastricht -The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"-Maastricht University -Institute of Data Science Paul-Henri Spaaklaan 1, 6229 EN Maastricht -The Netherlands","institution_ids":["https://openalex.org/I34352273"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3173,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52306623,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"41","last_page":"46"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9990000128746033,"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":0.9990000128746033,"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/T12536","display_name":"Topological and Geometric Data Analysis","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10320","display_name":"Neural Networks and Applications","score":9.999999747378752e-05,"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/categorical-variables","display_name":"categorical variables","score":0.7940000295639038},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.9190000295639038},{"id":"https://openalex.org/keywords/assignment-matrix","display_name":"assignment matrix","score":0.9120000004768372},{"id":"https://openalex.org/keywords/co-occurrence-matrix","display_name":"co-occurrence matrix","score":0.9869999885559082},{"id":"https://openalex.org/keywords/one-hot-encoding","display_name":"one-hot encoding","score":0.9549999833106995},{"id":"https://openalex.org/keywords/feature-embedding","display_name":"feature embedding","score":0.3610000014305115}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.9407404661178589},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.7937297821044922},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6272856593132019},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5421813726425171},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.533094584941864},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5312290787696838},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5046042203903198},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.476900577545166},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.47442904114723206},{"id":"https://openalex.org/C162199024","wikidata":"https://www.wikidata.org/wiki/Q3085391","display_name":"Degree matrix","level":5,"score":0.43171826004981995},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4311102628707886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.382576584815979},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.329540491104126},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28871262073516846},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.1227622926235199},{"id":"https://openalex.org/C149530733","wikidata":"https://www.wikidata.org/wiki/Q5597091","display_name":"Graph power","level":4,"score":0.08376181125640869},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14428/esann/2023.es2023-32","doi":"https://doi.org/10.14428/esann/2023.es2023-32","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2023.es2023-32","pdf_url":"https://doi.org/10.14428/esann/2023.es2023-32","source":{"id":"https://openalex.org/S7407087818","display_name":"European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2023 proceesdings","raw_type":"proceedings-article"},{"id":"pmh:oai:cris.maastrichtuniversity.nl:openaire/e4c6d7ae-ca28-4a98-acb3-faa647b3e90b","doi":null,"is_oa":true,"landing_page_url":"https://cris.maastrichtuniversity.nl/en/publications/e4c6d7ae-ca28-4a98-acb3-faa647b3e90b","pdf_url":null,"source":{"id":"https://openalex.org/S4306402616","display_name":"Research Publications (Maastricht University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I34352273","host_organization_name":"Maastricht University","host_organization_lineage":["https://openalex.org/I34352273"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Wang, W, Bromuri, S & Dumontier, M 2023, Graph-based Categorical Embedding. in ESANN 2023 Proceedings - 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc, pp. 41-46, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2023, Bruges, Belgium, 4/10/23. https://doi.org/10.14428/esann/2023.ES2023-32","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"doi:10.14428/esann/2023.es2023-32","doi":"https://doi.org/10.14428/esann/2023.es2023-32","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2023.es2023-32","pdf_url":"https://doi.org/10.14428/esann/2023.es2023-32","source":{"id":"https://openalex.org/S7407087818","display_name":"European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2023 proceesdings","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386815495.pdf","grobid_xml":"https://content.openalex.org/works/W4386815495.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W145476170","https://openalex.org/W1501856433","https://openalex.org/W2002394060","https://openalex.org/W2116341502","https://openalex.org/W2153579005","https://openalex.org/W2464785945","https://openalex.org/W2626778328","https://openalex.org/W2765449478","https://openalex.org/W2811124557","https://openalex.org/W2962756421","https://openalex.org/W3020873385","https://openalex.org/W3034618665","https://openalex.org/W3166778837","https://openalex.org/W4200554196"],"related_works":["https://openalex.org/W4295768228","https://openalex.org/W3084640753","https://openalex.org/W2464785945","https://openalex.org/W4306962677","https://openalex.org/W3026937485","https://openalex.org/W3095609162","https://openalex.org/W4281786792","https://openalex.org/W2962981362","https://openalex.org/W2803303416","https://openalex.org/W4243358255"],"abstract_inverted_index":{"Categorical":[0],"features":[1,45],"are":[2],"a":[3,59,101],"challenge":[4],"for":[5,67],"most":[6],"machine":[7,28],"learning":[8,29],"algorithms":[9],"that":[10,93],"only":[11],"accept":[12],"numerical":[13,112],"vectors":[14],"in":[15,104],"input.However,":[16],"the":[17,25,38,55,75,79,86,94],"emergence":[18],"of":[19,27,40,58],"graph":[20,42,61],"neural":[21,62],"networks":[22],"is":[23,35,97],"revolutionizing":[24],"application":[26],"models":[30],"to":[31,37,64],"traditional":[32],"data":[33,108],"sets.This":[34],"thanks":[36],"possibility":[39],"introducing":[41],"relationships":[43],"amongst":[44],"and":[46,82,99,113],"samples.In":[47],"this":[48],"contribution,":[49],"we":[50],"describe":[51],"an":[52,72],"algorithm":[53,95],"leveraging":[54],"assignment":[56],"matrix":[57,74,77],"DiffPool":[60],"network":[63],"calculate":[65],"embeddings":[66],"categorical":[68,80,90,114],"features,":[69],"using":[70],"as":[71,83],"adjacency":[73],"co-occurrence":[76],"between":[78],"values":[81],"nodes":[84],"feature":[85],"one":[87],"hot":[88],"encoded":[89],"values.We":[91],"show":[92],"proposed":[96],"scalable":[98],"presents":[100],"competitive":[102],"performance":[103],"three":[105],"publicly":[106],"available":[107],"sets":[109],"presenting":[110],"both":[111],"values.":[115],"41":[116]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2023-09-18T00:00:00"}