{"id":"https://openalex.org/W4385562565","doi":"https://doi.org/10.1145/3580305.3599451","title":"On Structural Expressive Power of Graph Transformers","display_name":"On Structural Expressive Power of Graph Transformers","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385562565","doi":"https://doi.org/10.1145/3580305.3599451"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599451","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5101649969","display_name":"Wenhao Zhu","orcid":"https://orcid.org/0000-0002-0249-2817"},"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":true,"raw_author_name":"Wenhao Zhu","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055005223","display_name":"Tianyu Wen","orcid":null},"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":"Tianyu Wen","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","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, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016772122","display_name":"Liang Wang","orcid":"https://orcid.org/0000-0001-5353-7803"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Wang","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073856221","display_name":"Bo Zheng","orcid":"https://orcid.org/0000-0002-4037-6315"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zheng","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101649969"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":1.3754,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.84770533,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3628","last_page":"3637"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9951000213623047,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.637734591960907},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5921381115913391},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5302514433860779},{"id":"https://openalex.org/keywords/graph-property","display_name":"Graph property","score":0.4761226773262024},{"id":"https://openalex.org/keywords/graph-isomorphism","display_name":"Graph isomorphism","score":0.4688884913921356},{"id":"https://openalex.org/keywords/subgraph-isomorphism-problem","display_name":"Subgraph isomorphism problem","score":0.43043047189712524},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4196015000343323},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40205320715904236},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.22999969124794006},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.17621636390686035}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.637734591960907},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5921381115913391},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5302514433860779},{"id":"https://openalex.org/C64339825","wikidata":"https://www.wikidata.org/wiki/Q722659","display_name":"Graph property","level":5,"score":0.4761226773262024},{"id":"https://openalex.org/C61665672","wikidata":"https://www.wikidata.org/wiki/Q303100","display_name":"Graph isomorphism","level":4,"score":0.4688884913921356},{"id":"https://openalex.org/C131992880","wikidata":"https://www.wikidata.org/wiki/Q2528185","display_name":"Subgraph isomorphism problem","level":3,"score":0.43043047189712524},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4196015000343323},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40205320715904236},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.22999969124794006},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.17621636390686035},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599451","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6600000262260437,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G3085993365","display_name":null,"funder_award_id":"(Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G37568934","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5249178904","display_name":null,"funder_award_id":"Grant No. 6","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7726157001","display_name":null,"funder_award_id":"Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7830412908","display_name":null,"funder_award_id":"62276006","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1780364926","https://openalex.org/W2062165320","https://openalex.org/W2114704115","https://openalex.org/W2142498761","https://openalex.org/W2176516200","https://openalex.org/W2743930031","https://openalex.org/W2962810718","https://openalex.org/W2963235422","https://openalex.org/W2965757697","https://openalex.org/W3005187920","https://openalex.org/W3016124664","https://openalex.org/W3041877813","https://openalex.org/W3081214609","https://openalex.org/W3092867907","https://openalex.org/W4212774754","https://openalex.org/W4221150379","https://openalex.org/W4287027946","https://openalex.org/W4287756632","https://openalex.org/W4297817821","https://openalex.org/W6681029592","https://openalex.org/W6775947557"],"related_works":["https://openalex.org/W2393701947","https://openalex.org/W167435155","https://openalex.org/W2743544037","https://openalex.org/W1988965627","https://openalex.org/W2363878907","https://openalex.org/W4226238757","https://openalex.org/W88519367","https://openalex.org/W3213765825","https://openalex.org/W2890359346","https://openalex.org/W4213101802"],"abstract_inverted_index":{"Graph":[0],"Transformer":[1,103],"has":[2,20],"recently":[3],"received":[4],"wide":[5,85],"attention":[6],"in":[7],"the":[8,27,64,75,91,110,123,133,146,154,182,196],"research":[9],"community":[10],"with":[11],"its":[12,16],"outstanding":[13],"performance,":[14],"yet":[15],"structural":[17,65,126,162],"expressive":[18,118,183],"power":[19,67,93,119,184],"not":[21],"been":[22],"well":[23],"analyzed.":[24],"Inspired":[25],"by":[26,100,122,145],"connections":[28],"between":[29],"Weisfeiler-Lehman":[30,49],"(WL)":[31],"graph":[32,36,53,69,88,116,136,186],"isomorphism":[33,54],"test":[34,43,55,77,96,140],"and":[35,90,128,141,157,177,188,191],"neural":[37],"network":[38,104],"(GNN),":[39],"we":[40,113,152],"introduce":[41],"SEG-WL":[42,76,95,111],"(Structural":[44],"Encoding":[45],"enhanced":[46],"G":[47],"lobal":[48],"test),":[50],"a":[51,58,84,101,159,175],"generalized":[52],"algorithm":[56],"as":[57],"powerful":[59],"theoretical":[60,172],"tool":[61],"for":[62,180],"exploring":[63],"discriminative":[66],"of":[68,87,94,125,135,185,198],"Transformers.":[70],"We":[71],"theoretically":[72],"prove":[73],"that":[74,131],"is":[78,120],"an":[79],"expressivity":[80,134],"upper":[81],"bound":[82],"on":[83],"range":[86],"Transformers,":[89,187],"representational":[92],"can":[97],"be":[98],"approximated":[99],"simple":[102],"arbitrarily":[105],"under":[106],"certain":[107],"conditions.":[108],"With":[109],"test,":[112],"show":[114],"how":[115],"Transformers'":[117],"determined":[121],"design":[124],"encodings,":[127],"present":[129],"conditions":[130],"make":[132],"Transformers":[137],"beyond":[138],"WL":[139],"GNNs.":[142],"Moreover,":[143],"motivated":[144],"popular":[147],"shortest":[148],"path":[149],"distance":[150],"encoding,":[151],"follow":[153],"theory-oriented":[155],"principles":[156],"develop":[158],"provably":[160],"stronger":[161],"encoding":[163],"method,":[164],"Shortest":[165],"Path":[166],"Induced":[167],"Subgraph":[168],"(SPIS)":[169],"encoding.":[170],"Our":[171],"findings":[173],"provide":[174],"novel":[176],"practical":[178],"paradigm":[179],"investigating":[181],"extensive":[189],"synthetic":[190],"real-world":[192],"experiments":[193],"empirically":[194],"verify":[195],"strengths":[197],"our":[199],"proposed":[200],"methods.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}