{"id":"https://openalex.org/W3192421672","doi":"https://doi.org/10.24963/ijcai.2021/425","title":"Multi-hop Attention Graph Neural Networks","display_name":"Multi-hop Attention Graph Neural Networks","publication_year":2021,"publication_date":"2021-08-01","ids":{"openalex":"https://openalex.org/W3192421672","doi":"https://doi.org/10.24963/ijcai.2021/425","mag":"3192421672"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2021/425","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/425","pdf_url":"https://www.ijcai.org/proceedings/2021/0425.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2021/0425.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102889910","display_name":"Guangtao Wang","orcid":"https://orcid.org/0000-0003-2766-0917"},"institutions":[{"id":"https://openalex.org/I72427458","display_name":"JDSU (United States)","ror":"https://ror.org/01a5v8x09","country_code":"US","type":"company","lineage":["https://openalex.org/I72427458"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Guangtao Wang","raw_affiliation_strings":["JD AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD AI Research","institution_ids":["https://openalex.org/I72427458"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078337825","display_name":"Rex Ying","orcid":"https://orcid.org/0000-0002-5856-5229"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rex Ying","raw_affiliation_strings":["Computer Science, Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100632700","display_name":"Jing Huang","orcid":"https://orcid.org/0000-0002-3294-5725"},"institutions":[{"id":"https://openalex.org/I72427458","display_name":"JDSU (United States)","ror":"https://ror.org/01a5v8x09","country_code":"US","type":"company","lineage":["https://openalex.org/I72427458"]},{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Huang","raw_affiliation_strings":["JD AI Research","Computer Science, Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD AI Research","institution_ids":["https://openalex.org/I72427458"]},{"raw_affiliation_string":"Computer Science, Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091272738","display_name":"Jure Leskovec","orcid":"https://orcid.org/0000-0002-5411-923X"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jure Leskovec","raw_affiliation_strings":["Computer Science, Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, Stanford University","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102889910"],"corresponding_institution_ids":["https://openalex.org/I72427458"],"apc_list":null,"apc_paid":null,"fwci":10.3537,"has_fulltext":true,"cited_by_count":110,"citation_normalized_percentile":{"value":0.9856183,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3089","last_page":"3096"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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/T10028","display_name":"Topic Modeling","score":0.9817000031471252,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9736999869346619,"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":0.6834842562675476},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5516711473464966},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5117983222007751},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.507899820804596},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.484971284866333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3602887988090515},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20738458633422852}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6834842562675476},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5516711473464966},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5117983222007751},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.507899820804596},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.484971284866333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3602887988090515},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20738458633422852}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2021/425","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/425","pdf_url":"https://www.ijcai.org/proceedings/2021/0425.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2021/425","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/425","pdf_url":"https://www.ijcai.org/proceedings/2021/0425.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":88,"referenced_works":["https://openalex.org/W1533230146","https://openalex.org/W1971368490","https://openalex.org/W2097998348","https://openalex.org/W2127795553","https://openalex.org/W2133564696","https://openalex.org/W2153959628","https://openalex.org/W2165874743","https://openalex.org/W2201741039","https://openalex.org/W2250184916","https://openalex.org/W2432356473","https://openalex.org/W2468907370","https://openalex.org/W2519887557","https://openalex.org/W2604314403","https://openalex.org/W2624431344","https://openalex.org/W2728059831","https://openalex.org/W2784814091","https://openalex.org/W2786460182","https://openalex.org/W2787740662","https://openalex.org/W2788284887","https://openalex.org/W2792839479","https://openalex.org/W2804057010","https://openalex.org/W2805516822","https://openalex.org/W2809418595","https://openalex.org/W2896457183","https://openalex.org/W2907079035","https://openalex.org/W2907492528","https://openalex.org/W2907957572","https://openalex.org/W2909137510","https://openalex.org/W2914592219","https://openalex.org/W2940243131","https://openalex.org/W2941033805","https://openalex.org/W2948598318","https://openalex.org/W2949434543","https://openalex.org/W2949865801","https://openalex.org/W2949945331","https://openalex.org/W2950393809","https://openalex.org/W2951515642","https://openalex.org/W2963241951","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2963415211","https://openalex.org/W2963858333","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2964114465","https://openalex.org/W2964116313","https://openalex.org/W2964308564","https://openalex.org/W2964321699","https://openalex.org/W2970836468","https://openalex.org/W2970843311","https://openalex.org/W2978508283","https://openalex.org/W2981525344","https://openalex.org/W2988237903","https://openalex.org/W2989205137","https://openalex.org/W2989915885","https://openalex.org/W2997347790","https://openalex.org/W3006524832","https://openalex.org/W3016242716","https://openalex.org/W3021975806","https://openalex.org/W3031047561","https://openalex.org/W3034758281","https://openalex.org/W3034772996","https://openalex.org/W3035134435","https://openalex.org/W3035203190","https://openalex.org/W3042770487","https://openalex.org/W3100078588","https://openalex.org/W3103296573","https://openalex.org/W3105136071","https://openalex.org/W3200504467","https://openalex.org/W4210257598","https://openalex.org/W4288088467","https://openalex.org/W4288089072","https://openalex.org/W4288335984","https://openalex.org/W4289389616","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4297895859","https://openalex.org/W4385245566","https://openalex.org/W4394666973","https://openalex.org/W6691737872","https://openalex.org/W6748419877","https://openalex.org/W6757634740","https://openalex.org/W6759363029","https://openalex.org/W6768314895","https://openalex.org/W6791858558","https://openalex.org/W6803771590","https://openalex.org/W6863631769","https://openalex.org/W6864014924"],"related_works":["https://openalex.org/W2046435967","https://openalex.org/W4231775656","https://openalex.org/W2383646825","https://openalex.org/W2371018915","https://openalex.org/W2354191502","https://openalex.org/W1972225038","https://openalex.org/W3134658850","https://openalex.org/W2355938171","https://openalex.org/W2780079842","https://openalex.org/W2115091349"],"abstract_inverted_index":{"Self-attention":[0],"mechanism":[1,42],"in":[2,125,135],"graph":[3,13,149,161,202],"neural":[4],"networks":[5],"(GNNs)":[6],"led":[7],"to":[8,70,111,173],"state-of-the-art":[9,168,181,206],"performance":[10,192,214],"on":[11,32,108,153,182,193,207],"many":[12],"representation":[14,34],"learning":[15],"tasks.":[16],"Currently,":[17],"at":[18],"every":[19,76,95,136],"layer,":[20,137],"attention":[21,41,79,84,109],"is":[22],"computed":[23],"between":[24,117],"connected":[25,52],"pairs":[26],"of":[27,35,78,97,120],"nodes":[28,47],"and":[29,127,138,185,209],"depends":[30],"solely":[31],"the":[33,36,83,87,91,98,118,159,179,190],"two":[37],"nodes.":[38,122],"However,":[39],"such":[40],"does":[43],"not":[44,50],"account":[45,113],"for":[46,94,114],"that":[48,129,143,165],"are":[49],"directly":[51],"but":[53],"provide":[54],"important":[55],"network":[56],"context.":[57],"Here":[58],"we":[59],"propose":[60],"Multi-hop":[61],"Attention":[62],"Graph":[63,197],"Neural":[64],"Network":[65],"(MAGNA),":[66],"a":[67,105,140,194],"principled":[68],"way":[69],"incorporate":[71],"multi-hop":[72],"context":[73],"information":[74,134,147],"into":[75],"layer":[77,96],"computation.":[80],"MAGNA":[81,103,130,166,170,187,204],"diffuses":[82],"scores":[85],"across":[86,211],"network,":[88],"which":[89],"increases":[90],"receptive":[92],"field":[93],"GNN.":[99],"Unlike":[100],"previous":[101,180],"approaches,":[102],"uses":[104],"diffusion":[106],"prior":[107],"values,":[110],"efficiently":[112],"all":[115],"paths":[116],"pair":[119],"disconnected":[121],"We":[123],"demonstrate":[124],"theory":[126],"experiments":[128],"captures":[131],"large-scale":[132,195],"structural":[133],"has":[139],"low-pass":[141],"effect":[142],"eliminates":[144],"noisy":[145],"high-frequency":[146],"from":[148],"data.":[150],"Experimental":[151],"results":[152],"node":[154],"classification":[155],"as":[156,158],"well":[157],"knowledge":[160,201],"completion":[162,203],"benchmarks":[163],"show":[164],"achieves":[167,171],"results:":[169],"up":[172],"5.7%":[174],"relative":[175],"error":[176],"reduction":[177],"over":[178],"Cora,":[183],"Citeseer,":[184],"Pubmed.":[186],"also":[188],"obtains":[189],"best":[191],"Open":[196],"Benchmark":[198],"dataset.":[199],"On":[200],"advances":[205],"WN18RR":[208],"FB15k-237":[210],"four":[212],"different":[213],"metrics.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":28},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":27},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}