{"id":"https://openalex.org/W2810777031","doi":"https://doi.org/10.1145/3220547.3220558","title":"Predicting competitor links in company networks","display_name":"Predicting competitor links in company networks","publication_year":2018,"publication_date":"2018-06-15","ids":{"openalex":"https://openalex.org/W2810777031","doi":"https://doi.org/10.1145/3220547.3220558","mag":"2810777031"},"language":"en","primary_location":{"id":"doi:10.1145/3220547.3220558","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3220547.3220558","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets","raw_type":"proceedings-article"},"type":"dataset","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/A5040575852","display_name":"C\u00e9sar de Pablo-S\u00e1nchez","orcid":null},"institutions":[{"id":"https://openalex.org/I133498377","display_name":"Banco Bilbao Vizcaya Argentaria (Spain)","ror":"https://ror.org/0537dnd74","country_code":"ES","type":"company","lineage":["https://openalex.org/I133498377"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"C. de Pablo-S\u00e1nchez","raw_affiliation_strings":["BBVA Data & Analytics, Madrid, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BBVA Data & Analytics, Madrid, Spain","institution_ids":["https://openalex.org/I133498377"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087857282","display_name":"Elena T. Herruzo","orcid":null},"institutions":[{"id":"https://openalex.org/I133498377","display_name":"Banco Bilbao Vizcaya Argentaria (Spain)","ror":"https://ror.org/0537dnd74","country_code":"ES","type":"company","lineage":["https://openalex.org/I133498377"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"E. T. Herruzo","raw_affiliation_strings":["BBVA Data & Analytics, Madrid, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BBVA Data & Analytics, Madrid, Spain","institution_ids":["https://openalex.org/I133498377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101182394","display_name":"Alberto Rubio","orcid":null},"institutions":[{"id":"https://openalex.org/I133498377","display_name":"Banco Bilbao Vizcaya Argentaria (Spain)","ror":"https://ror.org/0537dnd74","country_code":"ES","type":"company","lineage":["https://openalex.org/I133498377"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Alberto Rubio","raw_affiliation_strings":["BBVA Data & Analytics, Madrid, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BBVA Data & Analytics, Madrid, Spain","institution_ids":["https://openalex.org/I133498377"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I133498377"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9943000078201294,"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":0.9943000078201294,"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/T13976","display_name":"Web visibility and informetrics","score":0.9722999930381775,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9473999738693237,"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":0.7508726119995117},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.666558027267456},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5323615074157715},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5138893127441406},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4770733118057251},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.45616772770881653},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.4511874318122864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4270237982273102},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4171198308467865},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.41488218307495117},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3951234817504883}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7508726119995117},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.666558027267456},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5323615074157715},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5138893127441406},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4770733118057251},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.45616772770881653},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.4511874318122864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4270237982273102},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4171198308467865},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.41488218307495117},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3951234817504883},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3220547.3220558","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3220547.3220558","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.44999998807907104,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W34646664","https://openalex.org/W1835243625","https://openalex.org/W2963572185","https://openalex.org/W2997591727","https://openalex.org/W3123123374"],"related_works":["https://openalex.org/W2981877337","https://openalex.org/W3203938600","https://openalex.org/W2169074127","https://openalex.org/W83146503","https://openalex.org/W2163707935","https://openalex.org/W202723009","https://openalex.org/W2145955964","https://openalex.org/W2188612292","https://openalex.org/W4206462905","https://openalex.org/W2165396616"],"abstract_inverted_index":{"The":[0],"scored":[1],"task":[2],"at":[3],"FEIII":[4],"Challenge":[5],"2018":[6],"proposed":[7],"the":[8,19,34,80,83],"identification":[9],"of":[10,16,82],"competitor":[11,45],"relationships":[12],"in":[13,103],"a":[14,47,50,53,62],"network":[15],"companies":[17,70],"from":[18,73],"financial":[20],"and":[21,36,52,68,71,76,93],"IT":[22],"sectors.":[23],"This":[24],"article":[25],"describe":[26],"our":[27,37],"BBVA":[28],"Data":[29],"&":[30],"Analytics":[31],"submission":[32],"to":[33,43,78],"challenge":[35],"experiments":[38],"with":[39],"three":[40],"different":[41],"approaches":[42],"predict":[44],"links:":[46],"local":[48],"classifier,":[49],"recommender":[51,63],"relational":[54],"classifier.":[55],"Our":[56],"best":[57],"results":[58],"were":[59],"obtained":[60],"by":[61],"where":[64],"we":[65],"included":[66],"seed":[67],"non-seed":[69],"data":[72,109],"Thomson":[74],"Reuters":[75],"TNIC":[77],"set":[79],"probability":[81],"link.":[84],"In":[85],"perspective,":[86],"this":[87],"simple":[88],"approach":[89],"achieves":[90],"good":[91],"precision":[92],"moderate":[94],"recall,":[95],"but":[96],"also":[97],"highlights":[98],"that":[99],"solving":[100],"link":[101],"prediction":[102],"company":[104],"networks":[105],"may":[106],"require":[107],"additional":[108],"sources,":[110],"larger":[111],"training":[112],"sets":[113],"or":[114],"more":[115],"elaborated":[116],"models.":[117]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}