{"id":"https://openalex.org/W2020658122","doi":"https://doi.org/10.1145/2396761.2398449","title":"Mining competitive relationships by learning across heterogeneous networks","display_name":"Mining competitive relationships by learning across heterogeneous networks","publication_year":2012,"publication_date":"2012-10-29","ids":{"openalex":"https://openalex.org/W2020658122","doi":"https://doi.org/10.1145/2396761.2398449","mag":"2020658122"},"language":"en","primary_location":{"id":"doi:10.1145/2396761.2398449","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2396761.2398449","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM international conference on Information and knowledge management","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/A5100397797","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0003-4140-8128"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Yang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044791875","display_name":"Jie Tang","orcid":"https://orcid.org/0000-0003-3487-4593"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Tang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025064181","display_name":"Jacklyne Keomany","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jacklyne Keomany","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084061205","display_name":"Yanting Zhao","orcid":"https://orcid.org/0000-0001-5273-694X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanting Zhao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003324011","display_name":"Juanzi Li","orcid":"https://orcid.org/0000-0002-6244-0664"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juanzi Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047170063","display_name":"Ying Ding","orcid":"https://orcid.org/0000-0003-2567-2009"},"institutions":[{"id":"https://openalex.org/I4210119109","display_name":"Indiana University Bloomington","ror":"https://ror.org/02k40bc56","country_code":"US","type":"education","lineage":["https://openalex.org/I4210119109","https://openalex.org/I592451"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Ding","raw_affiliation_strings":["Indiana University, Bloomington, IN, USA"],"affiliations":[{"raw_affiliation_string":"Indiana University, Bloomington, IN, USA","institution_ids":["https://openalex.org/I4210119109"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100826112","display_name":"Tian Li","orcid":"https://orcid.org/0009-0008-7913-5894"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5120218293","display_name":"Liangwei Wang","orcid":"https://orcid.org/0000-0001-5989-9338"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangwei Wang","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shenzhen, China","Huawei Noah's Ark Lab, ShenZhen, China"],"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]},{"raw_affiliation_string":"Huawei Noah's Ark Lab, ShenZhen, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100397797"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.4552,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.81080933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1432","last_page":"1441"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9843000173568726,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.978600025177002,"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":0.7771477699279785},{"id":"https://openalex.org/keywords/competitor-analysis","display_name":"Competitor analysis","score":0.7521384358406067},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.48104289174079895},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.47953104972839355},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47588789463043213},{"id":"https://openalex.org/keywords/competitive-advantage","display_name":"Competitive advantage","score":0.4454367160797119},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4386318624019623},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4274912476539612},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4271474778652191},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.41002869606018066},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4080705940723419},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.12528634071350098},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1018018126487732}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7771477699279785},{"id":"https://openalex.org/C127576917","wikidata":"https://www.wikidata.org/wiki/Q624630","display_name":"Competitor analysis","level":2,"score":0.7521384358406067},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.48104289174079895},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.47953104972839355},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47588789463043213},{"id":"https://openalex.org/C58546491","wikidata":"https://www.wikidata.org/wiki/Q1150207","display_name":"Competitive advantage","level":2,"score":0.4454367160797119},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4386318624019623},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4274912476539612},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4271474778652191},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.41002869606018066},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4080705940723419},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.12528634071350098},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1018018126487732},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2396761.2398449","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2396761.2398449","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM international conference on Information and knowledge management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W24664060","https://openalex.org/W110568522","https://openalex.org/W1560512119","https://openalex.org/W1855283887","https://openalex.org/W1880262756","https://openalex.org/W2018165284","https://openalex.org/W2022867359","https://openalex.org/W2053081145","https://openalex.org/W2059004071","https://openalex.org/W2063774819","https://openalex.org/W2072459098","https://openalex.org/W2072907268","https://openalex.org/W2073415627","https://openalex.org/W2073614810","https://openalex.org/W2076219102","https://openalex.org/W2088750139","https://openalex.org/W2101196063","https://openalex.org/W2107743791","https://openalex.org/W2133299088","https://openalex.org/W2134746982","https://openalex.org/W2136161746","https://openalex.org/W2137813581","https://openalex.org/W2145418507","https://openalex.org/W2146964183","https://openalex.org/W2150461699","https://openalex.org/W2153080265","https://openalex.org/W2153635508","https://openalex.org/W2155186673","https://openalex.org/W2157975090","https://openalex.org/W2158545823","https://openalex.org/W2162913249","https://openalex.org/W2996402732","https://openalex.org/W3122139608","https://openalex.org/W4233135949","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2358804928","https://openalex.org/W4225710828","https://openalex.org/W1998528887","https://openalex.org/W2965538880","https://openalex.org/W2143282039","https://openalex.org/W155518088","https://openalex.org/W1520753694","https://openalex.org/W3192778866","https://openalex.org/W4313044692","https://openalex.org/W2335723827"],"abstract_inverted_index":{"Detecting":[0],"and":[1,44,79,86,126,152],"monitoring":[2],"competitors":[3],"is":[4,32,56],"fundamental":[5],"to":[6,9,39,49,121,132],"a":[7,25,57,117,128],"company":[8],"stay":[10],"ahead":[11],"in":[12,35,53],"the":[13,41,46,65,89,92,98,123,134,144,153,157],"global":[14],"market.":[15],"Existing":[16],"studies":[17],"mainly":[18],"focus":[19],"on":[20,147],"mining":[21,68],"competitive":[22,69,93],"relationships":[23,70,135],"within":[24],"single":[26],"data":[27,84,150],"source,":[28],"while":[29],"competing":[30],"information":[31],"usually":[33],"distributed":[34],"multiple":[36],"networks.":[37,75],"How":[38],"discover":[40],"underlying":[42],"patterns":[43,90,105],"utilize":[45],"heterogeneous":[47,74],"knowledge":[48],"avoid":[50],"biased":[51],"aspects":[52],"this":[54,61],"issue":[55],"challenging":[58],"problem.":[59],"In":[60],"paper,":[62],"we":[63],"study":[64,88],"problem":[66],"of":[67,106,159,165],"by":[71],"learning":[72,130],"across":[73],"We":[76,95,142],"use":[77],"Twitter":[78],"patent":[80],"records":[81],"as":[82],"our":[83,160],"sources":[85],"statistically":[87],"behind":[91],"relationships.":[94],"find":[96],"that":[97],"two":[99,124,148],"networks":[100,125],"exhibit":[101],"different":[102],"but":[103],"complementary":[104],"competitions.":[107],"Our":[108],"proposed":[109,145],"model,":[110,161],"Topical":[111],"Factor":[112],"Graph":[113],"Model":[114],"(TFGM),":[115],"defines":[116],"latent":[118],"topic":[119],"layer":[120],"bridge":[122],"learns":[127],"semi-supervised":[129],"model":[131,146],"classify":[133],"between":[136],"entities":[137],"(e.g.,":[138],"companies":[139],"or":[140],"products).":[141],"test":[143],"real":[149],"sets":[151],"experimental":[154],"results":[155],"validate":[156],"effectiveness":[158],"with":[162],"an":[163],"average":[164],"+46\\%":[166],"improvement":[167],"over":[168],"alternative":[169],"methods.":[170]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}