{"id":"https://openalex.org/W4415368155","doi":"https://doi.org/10.1109/isit63088.2025.11195516","title":"A Local Graph Limits Perspective on Sampling-Based GNNs","display_name":"A Local Graph Limits Perspective on Sampling-Based GNNs","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4415368155","doi":"https://doi.org/10.1109/isit63088.2025.11195516"},"language":null,"primary_location":{"id":"doi:10.1109/isit63088.2025.11195516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Information Theory (ISIT)","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/A5078547817","display_name":"Yeganeh Alimohammadi","orcid":"https://orcid.org/0000-0003-2760-2207"},"institutions":[{"id":"https://openalex.org/I2800817003","display_name":"Southern California University for Professional Studies","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]},{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yeganeh Alimohammadi","raw_affiliation_strings":["University of Southern California"],"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036583414","display_name":"Luana Ruiz","orcid":"https://orcid.org/0000-0002-9666-1211"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luana Ruiz","raw_affiliation_strings":["Johns Hopkins University"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001035442","display_name":"Amin Saberi","orcid":"https://orcid.org/0000-0002-7043-0722"},"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":"Amin Saberi","raw_affiliation_strings":["Stanford University"],"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5078547817"],"corresponding_institution_ids":["https://openalex.org/I1174212","https://openalex.org/I2800817003"],"apc_list":null,"apc_paid":null,"fwci":2.856,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.9237696,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9495999813079834,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9495999813079834,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9031000137329102,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.902899980545044,"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/graph","display_name":"Graph","score":0.5745999813079834},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4101000130176544},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.37689998745918274},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.3276999890804291},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3215999901294708},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.31850001215934753},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.310699999332428}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6363999843597412},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5745999813079834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5199000239372253},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5004000067710876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4948999881744385},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.37689998745918274},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.30820000171661377},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2935999929904938},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit63088.2025.11195516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2015375519","https://openalex.org/W2063313621","https://openalex.org/W2110596924","https://openalex.org/W2172204481","https://openalex.org/W2798598284","https://openalex.org/W2912636151","https://openalex.org/W2950850272","https://openalex.org/W3130869292","https://openalex.org/W3207343840","https://openalex.org/W3213412677","https://openalex.org/W4415368155"],"related_works":[],"abstract_inverted_index":{"We":[0,20,55,117],"offer":[1,81],"a":[2,35,75,82,101],"novel":[3],"theoretical":[4,83,120],"perspective":[5],"on":[6,31,51,58,92,123,136],"employing":[7],"sub":[8,67,137],"graph":[9,16,38,145],"sampling":[10],"methods":[11],"for":[12,85],"the":[13,44,48,52,59,63,66,70,86,97,111,143],"training":[14,29,47,71],"of":[15,34,43,46,61,65,77,89,96,99,113],"neural":[17],"networks":[18],"(GNNs).":[19],"prove":[21],"that,":[22],"under":[23],"mild":[24],"assumptions,":[25],"parameters":[26],"learned":[27],"from":[28],"GNNs":[30,90,115,134],"small":[32,93],"samples":[33,95],"large":[36,129],"input":[37],"are":[39],"within":[40],"an":[41],"\u220a-neighborhood":[42],"outcome":[45],"same":[49],"architecture":[50],"entire":[53],"graph.":[54],"derive":[56],"bounds":[57],"number":[60],"samples,":[62],"size":[64],"graph,":[68],"and":[69,108],"steps":[72],"required":[73],"as":[74,105],"function":[76],"\u220a.":[78],"Our":[79],"results":[80,121],"justification":[84],"empirical":[87],"success":[88],"trained":[91,135],"subgraph":[94],"graphs":[98,138],"interest,":[100],"paradigm":[102],"theoretically":[103],"formalized":[104],"transferability":[106],"[1]":[107],"which":[109],"forms":[110],"backbone":[112],"efficient":[114],"architectures.":[116],"validate":[118],"our":[119],"empirically":[122],"node":[124],"classification":[125],"tasks":[126],"using":[127],"moderately":[128],"citation":[130],"graphs,":[131],"demonstrating":[132],"that":[133],"12":[139],"\u00d7":[140],"smaller":[141],"than":[142],"original":[144],"achieve":[146],"comparable":[147],"performance.":[148]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-21T00:00:00"}
