{"id":"https://openalex.org/W4392902847","doi":"https://doi.org/10.1109/icassp48485.2024.10447704","title":"Graph Neural Networks are More Powerful than We Think","display_name":"Graph Neural Networks are More Powerful than We Think","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392902847","doi":"https://doi.org/10.1109/icassp48485.2024.10447704"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10447704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5034936665","display_name":"Charilaos I. Kanatsoulis","orcid":"https://orcid.org/0000-0002-0952-1561"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charilaos I. Kanatsoulis","raw_affiliation_strings":["University of Pennsylvania,Dept. of Electrical and Systems Engineering,Philadelphia,PA,USA","Dept. of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania,Dept. of Electrical and Systems Engineering,Philadelphia,PA,USA","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Dept. of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078862959","display_name":"Alejandro Ribeiro","orcid":"https://orcid.org/0000-0003-4230-9906"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alejandro Ribeiro","raw_affiliation_strings":["University of Pennsylvania,Dept. of Electrical and Systems Engineering,Philadelphia,PA,USA","Dept. of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania,Dept. of Electrical and Systems Engineering,Philadelphia,PA,USA","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Dept. of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I79576946"],"apc_list":null,"apc_paid":null,"fwci":3.8816,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.93990767,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"7550","last_page":"7554"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9805999994277954,"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/T10028","display_name":"Topic Modeling","score":0.9769999980926514,"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":0.7132797241210938},{"id":"https://openalex.org/keywords/expressive-power","display_name":"Expressive power","score":0.603546679019928},{"id":"https://openalex.org/keywords/power-graph-analysis","display_name":"Power graph analysis","score":0.5971131324768066},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5801673531532288},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5520251393318176},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4433198571205139},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43736159801483154},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3803125023841858}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7132797241210938},{"id":"https://openalex.org/C195818886","wikidata":"https://www.wikidata.org/wiki/Q5421724","display_name":"Expressive power","level":2,"score":0.603546679019928},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.5971131324768066},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5801673531532288},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5520251393318176},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4433198571205139},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43736159801483154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3803125023841858}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10447704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":37,"referenced_works":["https://openalex.org/W1991252559","https://openalex.org/W2104550457","https://openalex.org/W2150120952","https://openalex.org/W2943931381","https://openalex.org/W2943959761","https://openalex.org/W2962810718","https://openalex.org/W2970482593","https://openalex.org/W3005187920","https://openalex.org/W3016124664","https://openalex.org/W3033780210","https://openalex.org/W3035664258","https://openalex.org/W3092867907","https://openalex.org/W3112806341","https://openalex.org/W3126138172","https://openalex.org/W3187220393","https://openalex.org/W3215025646","https://openalex.org/W4221162104","https://openalex.org/W4287829537","https://openalex.org/W4287991183","https://openalex.org/W4288347098","https://openalex.org/W4309961428","https://openalex.org/W6754929296","https://openalex.org/W6757137939","https://openalex.org/W6758537753","https://openalex.org/W6760949768","https://openalex.org/W6762078418","https://openalex.org/W6762453230","https://openalex.org/W6763216505","https://openalex.org/W6765465941","https://openalex.org/W6766750229","https://openalex.org/W6775947557","https://openalex.org/W6779367026","https://openalex.org/W6784460448","https://openalex.org/W6784484382","https://openalex.org/W6789372467","https://openalex.org/W6796534871","https://openalex.org/W6810042608"],"related_works":["https://openalex.org/W3006338902","https://openalex.org/W1679944736","https://openalex.org/W4368755543","https://openalex.org/W4377142566","https://openalex.org/W3088104186","https://openalex.org/W1543023114","https://openalex.org/W4245709619","https://openalex.org/W85088162","https://openalex.org/W2294734161","https://openalex.org/W3081531507"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"are":[4,33],"powerful":[5],"architectures":[6],"that":[7,24,31,70,102],"have":[8],"demonstrated":[9],"remarkable":[10],"performance":[11],"in":[12],"various":[13],"node-level":[14],"and":[15,30,51,122],"graph-level":[16],"tasks.":[17],"Despite":[18],"this":[19,44],"success,":[20],"prominent":[21],"analysis":[22,113],"shows":[23],"their":[25,108],"representation":[26],"power":[27,55],"is":[28],"limited":[29],"they":[32],"at":[34],"most":[35],"as":[36,38],"expressive":[37,54,109],"the":[39,53,61,65,85,88,92,98,104,124,127],"Weisfeiler-Lehman":[40],"(WL)":[41],"test.":[42,90],"In":[43],"paper,":[45],"we":[46],"take":[47],"a":[48],"different":[49,81],"approach":[50,96],"analyze":[52],"of":[56,64,87,100,107,126],"GNNs":[57,71,101],"with":[58,80],"respect":[59],"to":[60],"spectral":[62],"decomposition":[63],"graph":[66,115],"operators.":[67],"We":[68],"prove":[69],"can":[72],"produce":[73],"distinct":[74],"equivariant":[75],"outputs":[76],"for":[77],"all":[78],"graphs":[79],"eigenvalues,":[82],"therefore":[83],"surpassing":[84],"limitations":[86],"WL":[89],"On":[91],"practical":[93],"front,":[94],"our":[95,119],"enables":[97],"design":[99],"unlock":[103],"full":[105],"potential":[106],"power.":[110],"Thorough":[111],"experimental":[112],"on":[114],"classification":[116],"datasets":[117],"supports":[118],"theoretical":[120],"findings":[121],"showcases":[123],"effectiveness":[125],"proposed":[128],"approach.":[129]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
