{"id":"https://openalex.org/W4367046943","doi":"https://doi.org/10.1145/3543507.3583511","title":"PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction","display_name":"PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction","publication_year":2023,"publication_date":"2023-04-26","ids":{"openalex":"https://openalex.org/W4367046943","doi":"https://doi.org/10.1145/3543507.3583511"},"language":"en","primary_location":{"id":"doi:10.1145/3543507.3583511","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3543507.3583511","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3543507.3583511","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 ACM Web Conference 2023","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3543507.3583511","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101412768","display_name":"Shichang Zhang","orcid":"https://orcid.org/0000-0003-0954-5018"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shichang Zhang","raw_affiliation_strings":["University of California, Los Angeles, USA"],"raw_orcid":"https://orcid.org/0000-0003-0954-5018","affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040806743","display_name":"Jiani Zhang","orcid":"https://orcid.org/0000-0003-0074-6761"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiani Zhang","raw_affiliation_strings":["Amazon, USA"],"raw_orcid":"https://orcid.org/0000-0003-0074-6761","affiliations":[{"raw_affiliation_string":"Amazon, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101675531","display_name":"Xiang Song","orcid":"https://orcid.org/0000-0001-5030-5054"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Song","raw_affiliation_strings":["Amazon, USA"],"raw_orcid":"https://orcid.org/0000-0001-5030-5054","affiliations":[{"raw_affiliation_string":"Amazon, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016551629","display_name":"Soji Adeshina","orcid":"https://orcid.org/0000-0003-3945-3640"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Soji Adeshina","raw_affiliation_strings":["Amazon, USA"],"raw_orcid":"https://orcid.org/0000-0003-3945-3640","affiliations":[{"raw_affiliation_string":"Amazon, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060763203","display_name":"Da Zheng","orcid":"https://orcid.org/0000-0001-8115-5415"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Da Zheng","raw_affiliation_strings":["Amazon, USA"],"raw_orcid":"https://orcid.org/0000-0001-8115-5415","affiliations":[{"raw_affiliation_string":"Amazon, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035605036","display_name":"Christos Faloutsos","orcid":"https://orcid.org/0000-0003-2996-9790"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christos Faloutsos","raw_affiliation_strings":["Carnegie Mellon University, USA and Amazon, USA"],"raw_orcid":"https://orcid.org/0000-0003-2996-9790","affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, USA and Amazon, USA","institution_ids":["https://openalex.org/I1311688040","https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025213473","display_name":"Yizhou Sun","orcid":"https://orcid.org/0000-0003-1812-6843"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yizhou Sun","raw_affiliation_strings":["University of California, Los Angeles, USA and Amazon, USA"],"raw_orcid":"https://orcid.org/0000-0003-1812-6843","affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, USA and Amazon, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":37,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3784","last_page":"3793"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9984999895095825,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9984999895095825,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9983999729156494,"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":0.9764000177383423,"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/interpretability","display_name":"Interpretability","score":0.8534855842590332},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8052350878715515},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6770700216293335},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.6355940103530884},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5472256541252136},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4645409882068634},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.45419245958328247},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.45234501361846924},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4303815960884094},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3466957211494446},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.11631479859352112},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07450452446937561}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8534855842590332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8052350878715515},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6770700216293335},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.6355940103530884},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5472256541252136},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4645409882068634},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.45419245958328247},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45234501361846924},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4303815960884094},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3466957211494446},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.11631479859352112},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07450452446937561}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3543507.3583511","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3543507.3583511","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3543507.3583511","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 ACM Web Conference 2023","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3543507.3583511","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3543507.3583511","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3543507.3583511","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 ACM Web Conference 2023","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7066469448","display_name":"DMREF/Collaborative Research: Inverse Design of Architected Materials with Prescribed Behaviors via Graph Based Networks and Additive Manufacturing","funder_award_id":"2119643","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7653253671","display_name":null,"funder_award_id":"2211557,1937599,2119643","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8659250076","display_name":"SHF: Medium: Automating High Level Synthesis via Graph-Centric Deep Learning","funder_award_id":"2211557","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"},{"id":"https://openalex.org/F4320307791","display_name":"Cisco Systems","ror":"https://ror.org/03yt1ez60"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4367046943.pdf","grobid_xml":"https://content.openalex.org/works/W4367046943.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W1529533208","https://openalex.org/W2022322548","https://openalex.org/W2026417691","https://openalex.org/W2047205327","https://openalex.org/W2065663455","https://openalex.org/W2117831564","https://openalex.org/W2122595065","https://openalex.org/W2134584261","https://openalex.org/W2282821441","https://openalex.org/W2622381546","https://openalex.org/W2750144484","https://openalex.org/W2798435682","https://openalex.org/W2907492528","https://openalex.org/W2942109571","https://openalex.org/W2950401953","https://openalex.org/W2962858109","https://openalex.org/W2966349618","https://openalex.org/W2991380435","https://openalex.org/W2999817249","https://openalex.org/W3098307636","https://openalex.org/W3101366597","https://openalex.org/W3116637551","https://openalex.org/W3131457744","https://openalex.org/W3152893301","https://openalex.org/W3156738579","https://openalex.org/W3172525852","https://openalex.org/W3211143493","https://openalex.org/W3211651881","https://openalex.org/W3213637582"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W4312407344","https://openalex.org/W2894289927"],"abstract_inverted_index":{"Transparency":[0],"and":[1,22,46,71,78,111,134,151,158],"accountability":[2],"have":[3,33],"become":[4],"major":[5],"concerns":[6],"for":[7,15,56,97,147],"black-box":[8],"machine":[9],"learning":[10],"(ML)":[11],"models.":[12,28],"Proper":[13],"explanations":[14,104,119,141],"the":[16,63,131],"model":[17,20,109],"behavior":[18],"increase":[19],"transparency":[21],"help":[23],"researchers":[24],"develop":[25],"more":[26],"accountable":[27],"Graph":[29],"neural":[30],"networks":[31],"(GNN)":[32],"recently":[34],"shown":[35],"superior":[36],"performance":[37],"in":[38,62,167],"many":[39],"graph":[40,113],"ML":[41],"problems":[42],"than":[43],"traditional":[44],"methods,":[45],"explaining":[47],"them":[48],"has":[49],"attracted":[50],"increased":[51],"interest.":[52],"However,":[53],"GNN":[54,69,85,95],"explanation":[55,86],"link":[57],"prediction":[58,100],"(LP)":[59],"is":[60,66],"lacking":[61],"literature.":[64],"LP":[65],"an":[67],"essential":[68],"task":[70],"corresponds":[72],"to":[73,137],"web":[74],"applications":[75],"like":[76],"recommendation":[77,148],"sponsored":[79],"search":[80],"on":[81,149],"web.":[82],"Given":[83],"existing":[84],"methods":[87],"only":[88],"address":[89],"node/graph-level":[90],"tasks,":[91],"we":[92],"propose":[93],"Path-based":[94],"Explanation":[96],"heterogeneous":[98],"Link":[99],"(PaGE-Link)":[101],"that":[102],"generates":[103],"with":[105],"connection":[106],"interpretability,":[107],"enjoys":[108],"scalability,":[110],"handles":[112],"heterogeneity.":[114],"Qualitatively,":[115],"PaGE-Link":[116,144],"can":[117],"generate":[118],"as":[120,161],"paths":[121],"connecting":[122],"a":[123],"node":[124],"pair,":[125],"which":[126],"naturally":[127],"captures":[128],"connections":[129],"between":[130],"two":[132],"nodes":[133],"easily":[135],"transfer":[136],"human-interpretable":[138],"explanations.":[139],"Quantitatively,":[140],"generated":[142],"by":[143,154,163],"improve":[145],"AUC":[146],"citation":[150],"user-item":[152],"graphs":[153],"9":[155],"-":[156],"35%":[157],"are":[159],"chosen":[160],"better":[162],"78.79%":[164],"of":[165],"responses":[166],"human":[168],"evaluation.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}