{"id":"https://openalex.org/W4403977002","doi":"https://doi.org/10.1109/tbdata.2024.3489418","title":"M-Graphormer: Multi-Channel Graph Transformer for Node Representation Learning","display_name":"M-Graphormer: Multi-Channel Graph Transformer for Node Representation Learning","publication_year":2024,"publication_date":"2024-11-01","ids":{"openalex":"https://openalex.org/W4403977002","doi":"https://doi.org/10.1109/tbdata.2024.3489418"},"language":"en","primary_location":{"id":"doi:10.1109/tbdata.2024.3489418","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2024.3489418","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Big Data","raw_type":"journal-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/A5081291162","display_name":"Xinglong Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinglong Chang","raw_affiliation_strings":["School of New Media and Communication, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of New Media and Communication, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102003658","display_name":"Jianrong Wang","orcid":"https://orcid.org/0000-0002-8980-1634"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianrong Wang","raw_affiliation_strings":["School of New Media and Communication, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-8980-1634","affiliations":[{"raw_affiliation_string":"School of New Media and Communication, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015866094","display_name":"Mingxiang Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingxiang Wen","raw_affiliation_strings":["School of New Media and Communication, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of New Media and Communication, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012375264","display_name":"Yingkui Wang","orcid":"https://orcid.org/0000-0002-2382-880X"},"institutions":[{"id":"https://openalex.org/I4403928323","display_name":"Tianjin Renai College","ror":"https://ror.org/03e5da851","country_code":null,"type":"education","lineage":["https://openalex.org/I4403928323"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingkui Wang","raw_affiliation_strings":["School of Intelligent Computing Engineering, Tianjin Renai College, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-2382-880X","affiliations":[{"raw_affiliation_string":"School of Intelligent Computing Engineering, Tianjin Renai College, Tianjin, China","institution_ids":["https://openalex.org/I4403928323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103195106","display_name":"Yuxiao Huang","orcid":"https://orcid.org/0000-0001-9394-805X"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuxiao Huang","raw_affiliation_strings":["Department of Data Science, George Washington University, Washington, DC, USA","Department of Data Science, George Washington University, NW, Washington, DC, USA"],"raw_orcid":"https://orcid.org/0000-0001-9394-805X","affiliations":[{"raw_affiliation_string":"Department of Data Science, George Washington University, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]},{"raw_affiliation_string":"Department of Data Science, George Washington University, NW, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8335,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78167617,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"11","issue":"4","first_page":"1867","last_page":"1878"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9059000015258789,"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":0.9059000015258789,"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":0.7717561721801758},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4381805658340454},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4288814961910248},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3266874849796295},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3239792585372925}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7717561721801758},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4381805658340454},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4288814961910248},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3266874849796295},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3239792585372925}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2024.3489418","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2024.3489418","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1763852702","display_name":null,"funder_award_id":"62276187","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W2010187764","https://openalex.org/W2019786737","https://openalex.org/W2470673105","https://openalex.org/W2508137831","https://openalex.org/W2962756421","https://openalex.org/W2964051675","https://openalex.org/W2998702685","https://openalex.org/W3012871709","https://openalex.org/W3039075121","https://openalex.org/W3090999459","https://openalex.org/W3093499132","https://openalex.org/W3128443161","https://openalex.org/W3139306118","https://openalex.org/W3168547639","https://openalex.org/W3217672792","https://openalex.org/W4206218111","https://openalex.org/W4224310669","https://openalex.org/W4281706128","https://openalex.org/W4282975557","https://openalex.org/W4283810673","https://openalex.org/W4292858593","https://openalex.org/W4294791395","https://openalex.org/W4367047312","https://openalex.org/W4376466686","https://openalex.org/W4385245566","https://openalex.org/W4386562631","https://openalex.org/W4392796768","https://openalex.org/W4393148093","https://openalex.org/W4393972734","https://openalex.org/W4394862809","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6752110883","https://openalex.org/W6767098714","https://openalex.org/W6771621015","https://openalex.org/W6784333009","https://openalex.org/W6784614190","https://openalex.org/W6784977419","https://openalex.org/W6787995345","https://openalex.org/W6796768152","https://openalex.org/W6796801894","https://openalex.org/W6802487873","https://openalex.org/W6803334427","https://openalex.org/W6810247653","https://openalex.org/W6811246986","https://openalex.org/W6845998438","https://openalex.org/W6846025074","https://openalex.org/W6846424470","https://openalex.org/W6846765560","https://openalex.org/W6847178786","https://openalex.org/W6849210429","https://openalex.org/W6856142792","https://openalex.org/W6860157398"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"the":[3,24,90,169,183,190],"Graph":[4,26,151],"Transformer":[5,27,152],"has":[6],"demonstrated":[7],"superiority":[8],"on":[9,71,100,160,178],"various":[10],"graph-level":[11],"tasks":[12],"by":[13],"facilitating":[14],"global":[15,52],"interactions":[16],"among":[17],"nodes.":[18],"However,":[19],"as":[20,30,32],"for":[21,168,182],"node-level":[22],"tasks,":[23],"existing":[25],"cannot":[28],"perform":[29,95],"well":[31],"expected.":[33],"Actually,":[34],"a":[35,38,59,96,130,149],"node":[36,55,85,136,170],"in":[37,89],"real-world":[39],"graph":[40,72,113],"does":[41],"not":[42],"necessarily":[43],"have":[44],"relationships":[45],"with":[46,104,138,164],"every":[47,84],"other":[48],"node,":[49],"and":[50,77,83,140],"this":[51,145],"interaction":[53,68,119],"weakens":[54],"features.":[56],"This":[57,121],"raises":[58],"fundamental":[60],"question:":[61],"should":[62],"we":[63,109,127,147],"partition":[64],"out":[65],"an":[66],"appropriate":[67],"channel":[69],"based":[70],"structure":[73],"so":[74],"that":[75,111,133,189],"noisy":[76],"irrelevant":[78],"information":[79,88],"will":[80],"be":[81],"filtered":[82],"can":[86,128],"aggregate":[87],"optimal":[91,118],"channel?":[92],"We":[93],"first":[94],"series":[97],"of":[98],"experiments":[99,175],"manually":[101],"created":[102],"graphs":[103],"varying":[105],"homophily":[106,166],"ratios.":[107],"Surprisingly,":[108],"observe":[110],"different":[112,165],"structures":[114],"indeed":[115],"require":[116],"distinct":[117],"channels.":[120],"leads":[122],"us":[123],"to":[124],"ask":[125],"whether":[126],"develop":[129],"partitioning":[131],"rule":[132],"ensures":[134],"each":[135],"interacts":[137],"relevant":[139],"valuable":[141],"targets.":[142],"To":[143],"overcome":[144],"challenge,":[146],"propose":[148],"novel":[150],"named":[153],"Multi-channel":[154,191],"Graphormer.":[155],"The":[156],"model":[157],"is":[158],"evaluated":[159],"six":[161],"network":[162],"datasets":[163,181],"ratios":[167],"classification":[171],"task.":[172,185],"Moreover,":[173],"comprehensive":[174],"are":[176],"conducted":[177],"two":[179],"real":[180],"recommendation":[184],"Experimental":[186],"results":[187],"show":[188],"Graphormer":[192],"surpasses":[193],"state-of-the-art":[194],"baselines,":[195],"demonstrating":[196],"superior":[197],"performance.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}