{"id":"https://openalex.org/W3153321424","doi":"https://doi.org/10.1145/3442381.3449896","title":"Improving Graph Neural Networks with Structural Adaptive Receptive Fields","display_name":"Improving Graph Neural Networks with Structural Adaptive Receptive Fields","publication_year":2021,"publication_date":"2021-04-19","ids":{"openalex":"https://openalex.org/W3153321424","doi":"https://doi.org/10.1145/3442381.3449896","mag":"3153321424"},"language":"en","primary_location":{"id":"doi:10.1145/3442381.3449896","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449896","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3442381.3449896","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052953923","display_name":"Xiaojun Ma","orcid":"https://orcid.org/0000-0001-6757-3055"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojun Ma","raw_affiliation_strings":["Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101674609","display_name":"Junshan Wang","orcid":"https://orcid.org/0000-0002-7382-4823"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junshan Wang","raw_affiliation_strings":["Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100734697","display_name":"Han Y. H. Chen","orcid":"https://orcid.org/0000-0001-9477-5541"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanyue Chen","raw_affiliation_strings":["Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088976879","display_name":"Guojie Song","orcid":"https://orcid.org/0000-0001-8295-2520"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guojie Song","raw_affiliation_strings":["Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2438","last_page":"2447"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9879000186920166,"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/T12676","display_name":"Machine Learning and ELM","score":0.9842000007629395,"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/receptive-field","display_name":"Receptive field","score":0.8287448883056641},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.700886607170105},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5610914826393127},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5008633136749268},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49503961205482483},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4886970818042755},{"id":"https://openalex.org/keywords/news-aggregator","display_name":"News aggregator","score":0.4233388304710388},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.42292797565460205},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3665791153907776},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35410213470458984},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10242220759391785}],"concepts":[{"id":"https://openalex.org/C19071747","wikidata":"https://www.wikidata.org/wiki/Q1755207","display_name":"Receptive field","level":2,"score":0.8287448883056641},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.700886607170105},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5610914826393127},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5008633136749268},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49503961205482483},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4886970818042755},{"id":"https://openalex.org/C180505990","wikidata":"https://www.wikidata.org/wiki/Q498267","display_name":"News aggregator","level":2,"score":0.4233388304710388},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.42292797565460205},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3665791153907776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35410213470458984},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10242220759391785},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3442381.3449896","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449896","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3442381.3449896","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449896","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1028735754","https://openalex.org/W1888005072","https://openalex.org/W2132452995","https://openalex.org/W2154851992","https://openalex.org/W2167250030","https://openalex.org/W2182190337","https://openalex.org/W2419501139","https://openalex.org/W2560674852","https://openalex.org/W2747329762","https://openalex.org/W2785397276","https://openalex.org/W2946721323","https://openalex.org/W2947665307","https://openalex.org/W2962756421","https://openalex.org/W2963415211","https://openalex.org/W2964420626","https://openalex.org/W2994710732","https://openalex.org/W2995471443","https://openalex.org/W2996084050","https://openalex.org/W3004136411","https://openalex.org/W3012816161","https://openalex.org/W3104097132","https://openalex.org/W3105705953"],"related_works":["https://openalex.org/W3036238356","https://openalex.org/W2767445978","https://openalex.org/W2603387358","https://openalex.org/W2570892890","https://openalex.org/W3092831610","https://openalex.org/W2012785328","https://openalex.org/W4206057490","https://openalex.org/W962287279","https://openalex.org/W3112734853","https://openalex.org/W4226363941"],"abstract_inverted_index":{"The":[0],"abundant":[1],"information":[2,56,90,114],"in":[3,15,29,153,163,175],"graphs":[4],"helps":[5],"us":[6],"to":[7,21,36,46,115,136,148],"learn":[8],"more":[9,171],"expressive":[10],"node":[11,87,138],"representations.":[12],"Different":[13],"nodes":[14],"the":[16,22,43,53,61,112,125,146,150,159],"neighborhood":[17],"have":[18],"different":[19],"importance":[20],"central":[23],"node.":[24],"Thus,":[25],"average":[26],"weight":[27],"aggregation":[28,95],"most":[30],"Graph":[31,70],"Neural":[32,71],"Networks":[33,72],"would":[34],"fail":[35],"model":[37,100],"such":[38],"difference.":[39],"GAT-based":[40],"models":[41],"introduce":[42],"attention":[44],"mechanism":[45],"solve":[47],"this":[48,66],"problem,":[49],"but":[50],"they":[51],"ignore":[52],"rich":[54],"structural":[55,89,102,113,165,173],"and":[57,91,140,169],"may":[58],"suffer":[59],"from":[60],"problem":[62],"of":[63,96,161],"over-smoothing.":[64],"In":[65],"paper,":[67],"we":[68,99,131],"propose":[69],"with":[73,88,120],"STructural":[74],"Adaptive":[75],"Receptive":[76],"fields":[77,118,128,167],"(STAR-GNN),":[78],"which":[79],"adaptively":[80,168],"construct":[81,116],"a":[82,133],"receptive":[83,117,127,154,166],"field":[84],"for":[85],"each":[86],"further":[92],"achieve":[93],"better":[94],"information.":[97,122],"Firstly,":[98],"local":[101],"distribution":[103],"based":[104],"on":[105],"anonymous":[106],"random":[107],"walks,":[108],"followed":[109],"by":[110],"using":[111],"guided":[119],"mutual":[121],"Then,":[123],"as":[124],"generated":[126],"are":[129],"irregular,":[130],"design":[132],"sub-graph":[134],"aggregator":[135],"boost":[137],"representations":[139],"theoretically":[141],"prove":[142],"that":[143],"it":[144],"has":[145],"ability":[147],"capture":[149],"complex":[151],"structures":[152],"fields.":[155],"Experimental":[156],"results":[157],"demonstrate":[158],"power":[160],"STAR-GNN":[162],"learning":[164],"encoding":[170],"informative":[172],"characteristics":[174],"real-world":[176],"networks.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}