{"id":"https://openalex.org/W4390647500","doi":"https://doi.org/10.1145/3639295","title":"View-based Explanations for Graph Neural Networks","display_name":"View-based Explanations for Graph Neural Networks","publication_year":2024,"publication_date":"2024-03-12","ids":{"openalex":"https://openalex.org/W4390647500","doi":"https://doi.org/10.1145/3639295"},"language":"en","primary_location":{"id":"doi:10.1145/3639295","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3639295","pdf_url":null,"source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2401.02086","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033903725","display_name":"Tingyang Chen","orcid":"https://orcid.org/0009-0008-5635-9326"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingyang Chen","raw_affiliation_strings":["Zhejiang University, Ningbo, China"],"raw_orcid":"https://orcid.org/0009-0008-5635-9326","affiliations":[{"raw_affiliation_string":"Zhejiang University, Ningbo, China","institution_ids":["https://openalex.org/I109935558","https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075248552","display_name":"Dazhuo Qiu","orcid":"https://orcid.org/0000-0002-1044-5252"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Dazhuo Qiu","raw_affiliation_strings":["Aalborg University, Aalborg, Denmark"],"raw_orcid":"https://orcid.org/0000-0002-1044-5252","affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071093153","display_name":"Yinghui Wu","orcid":"https://orcid.org/0000-0003-3991-5155"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yinghui Wu","raw_affiliation_strings":["Case Western Reserve University, Cleveland, USA"],"raw_orcid":"https://orcid.org/0000-0003-3991-5155","affiliations":[{"raw_affiliation_string":"Case Western Reserve University, Cleveland, USA","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019024971","display_name":"Arijit Khan","orcid":"https://orcid.org/0000-0002-7312-6312"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Arijit Khan","raw_affiliation_strings":["Aalborg University, Aalborg, Denmark"],"raw_orcid":"https://orcid.org/0000-0002-7312-6312","affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071521429","display_name":"Xiangyu Ke","orcid":"https://orcid.org/0000-0001-8082-7398"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyu Ke","raw_affiliation_strings":["Zhejiang University & Zhejiang University, Ningbo, China"],"raw_orcid":"https://orcid.org/0000-0001-8082-7398","affiliations":[{"raw_affiliation_string":"Zhejiang University & Zhejiang University, Ningbo, China","institution_ids":["https://openalex.org/I109935558","https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006238145","display_name":"Yunjun Gao","orcid":"https://orcid.org/0000-0003-3816-8450"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunjun Gao","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-3816-8450","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.612,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.93349749,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"2","issue":"1","first_page":"1","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991999864578247,"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":0.9991999864578247,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9983000159263611,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9871000051498413,"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":0.707344114780426},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.5706045031547546},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5585711002349854},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.4816916882991791},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.46960175037384033},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46065446734428406},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4534076750278473},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4476836025714874},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41814178228378296},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19009321928024292},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.17786940932273865}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.707344114780426},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.5706045031547546},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5585711002349854},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.4816916882991791},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.46960175037384033},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46065446734428406},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4534076750278473},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4476836025714874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41814178228378296},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19009321928024292},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.17786940932273865},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3639295","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3639295","pdf_url":null,"source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2401.02086","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.02086","pdf_url":"https://arxiv.org/pdf/2401.02086","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:pure.atira.dk:publications/1ebec6b1-a134-48a2-a483-15442a33f59c","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/1ebec6b1-a134-48a2-a483-15442a33f59c","pdf_url":"https://arxiv.org/pdf/2401.02086","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Chen, T, Qiu, D, Wu, Y, Khan, A, Ke, X & Gao, Y 2024, 'View-based Explanations for Graph Neural Networks', Proceedings of the ACM on Management of Data, vol. 2, no. 1, 40, pp. 1-27. https://doi.org/10.1145/3639295","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2401.02086","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.02086","pdf_url":"https://arxiv.org/pdf/2401.02086","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1492048597","display_name":null,"funder_award_id":"CNS-1932574, ECCS-1933279, CNS-2028748, OAC-2104007","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"},{"id":"https://openalex.org/G2872666361","display_name":null,"funder_award_id":"NNF22OC0072415","funder_id":"https://openalex.org/F4320322436","funder_display_name":"Novo Nordisk"},{"id":"https://openalex.org/G3188578225","display_name":null,"funder_award_id":"U23A20296","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3284713303","display_name":null,"funder_award_id":"ECCS-1933279","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G388053966","display_name":null,"funder_award_id":"CNS-1932574","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4304986854","display_name":"Collaborative Research: Online Data Stream Fusion and Deep Learning for Virtual Meter in Smart Power Distribution Systems","funder_award_id":"1933279","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5410940137","display_name":null,"funder_award_id":"62025206","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5494444537","display_name":null,"funder_award_id":"62025206, U23A20296","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"},{"id":"https://openalex.org/G5616988581","display_name":"Elements: Crowdsourced Materials Data Engine for Unpublished XRD Results","funder_award_id":"2104007","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6204808334","display_name":"CPS: DFG Joint: Medium: Collaborative Research: Data-Driven Secure Holonic control and Optimization for the Networked CPS (aDaptioN)","funder_award_id":"1932574","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6242636793","display_name":null,"funder_award_id":"CNS-2028748","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6390304386","display_name":"SaTC: CORE: Small: Scalable Cyber Attack Investigation using Declarative Queriesand Interrogative Analysis","funder_award_id":"2028748","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6437268572","display_name":null,"funder_award_id":"NNF22OC0072415","funder_id":"https://openalex.org/F4320325957","funder_display_name":"Novo Nordisk Fonden"},{"id":"https://openalex.org/G705446968","display_name":null,"funder_award_id":"2022A-237-G","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322436","display_name":"Novo Nordisk","ror":"https://ror.org/0435rc536"},{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"},{"id":"https://openalex.org/F4320325957","display_name":"Novo Nordisk Fonden","ror":"https://ror.org/04txyc737"},{"id":"https://openalex.org/F4320337392","display_name":"Division of Electrical, Communications and Cyber Systems","ror":"https://ror.org/01krpsy48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":88,"referenced_works":["https://openalex.org/W398859631","https://openalex.org/W569478347","https://openalex.org/W1522301498","https://openalex.org/W1849277567","https://openalex.org/W1883038075","https://openalex.org/W1977340881","https://openalex.org/W1989151913","https://openalex.org/W2008857988","https://openalex.org/W2024505010","https://openalex.org/W2062705952","https://openalex.org/W2081417442","https://openalex.org/W2099438806","https://openalex.org/W2102039273","https://openalex.org/W2107577105","https://openalex.org/W2110953678","https://openalex.org/W2132651631","https://openalex.org/W2140840007","https://openalex.org/W2143893055","https://openalex.org/W2164281374","https://openalex.org/W2170726034","https://openalex.org/W2480626043","https://openalex.org/W2584774953","https://openalex.org/W2593390416","https://openalex.org/W2608151934","https://openalex.org/W2786016794","https://openalex.org/W2788919350","https://openalex.org/W2790755967","https://openalex.org/W2804057010","https://openalex.org/W2899432611","https://openalex.org/W2907492528","https://openalex.org/W2916797267","https://openalex.org/W2922308772","https://openalex.org/W2944377111","https://openalex.org/W2949712504","https://openalex.org/W2951659295","https://openalex.org/W2962711740","https://openalex.org/W2962946486","https://openalex.org/W2963521729","https://openalex.org/W2963691697","https://openalex.org/W2963757395","https://openalex.org/W2964015378","https://openalex.org/W2972317931","https://openalex.org/W2983721890","https://openalex.org/W3000120900","https://openalex.org/W3032123378","https://openalex.org/W3033892090","https://openalex.org/W3082499364","https://openalex.org/W3093356333","https://openalex.org/W3100078588","https://openalex.org/W3103717137","https://openalex.org/W3105503635","https://openalex.org/W3105657048","https://openalex.org/W3116637551","https://openalex.org/W3130218089","https://openalex.org/W3136399186","https://openalex.org/W3139415452","https://openalex.org/W3165484655","https://openalex.org/W3179413940","https://openalex.org/W3190799236","https://openalex.org/W3192615599","https://openalex.org/W3197982863","https://openalex.org/W3206830733","https://openalex.org/W4205684821","https://openalex.org/W4210257598","https://openalex.org/W4226237846","https://openalex.org/W4230608978","https://openalex.org/W4244071313","https://openalex.org/W4285723986","https://openalex.org/W4286902437","https://openalex.org/W4287330432","https://openalex.org/W4287649558","https://openalex.org/W4289389616","https://openalex.org/W4289534000","https://openalex.org/W4289534042","https://openalex.org/W4294558607","https://openalex.org/W4296300780","https://openalex.org/W4298082126","https://openalex.org/W4303449616","https://openalex.org/W4308022988","https://openalex.org/W4310980124","https://openalex.org/W4311833010","https://openalex.org/W4313441777","https://openalex.org/W4321372691","https://openalex.org/W4321485347","https://openalex.org/W4382318226","https://openalex.org/W4407831773","https://openalex.org/W6685146747","https://openalex.org/W6786048916"],"related_works":["https://openalex.org/W2366403280","https://openalex.org/W1495108544","https://openalex.org/W2091301346","https://openalex.org/W3148229873","https://openalex.org/W4389760904","https://openalex.org/W2150160875","https://openalex.org/W4242223894","https://openalex.org/W2144629587","https://openalex.org/W3101224877","https://openalex.org/W2272988256"],"abstract_inverted_index":{"Generating":[0],"explanations":[1,34],"for":[2,35,64,144],"graph":[3,19,85],"neural":[4],"networks":[5],"(GNNs)":[6],"has":[7],"been":[8],"studied":[9],"to":[10,24,49,139,188,208,215],"understand":[11,25],"their":[12],"behaviors":[13],"in":[14,176,213],"analytical":[15],"tasks":[16],"such":[17],"as":[18],"classification.":[20],"Existing":[21],"approaches":[22],"aim":[23],"the":[26,116,150,234,246],"overall":[27],"results":[28],"of":[29,39,81,84,90,98,118,178,200,225,239,249],"GNNs":[30,175],"rather":[31],"than":[32],"providing":[33],"specific":[36,103],"class":[37,104],"labels":[38],"interest,":[40],"and":[41,87,101,134,182,237],"may":[42],"return":[43],"explanation":[44,72,75,78,92,142,170,218],"structures":[45],"that":[46,60,120,149,166,193],"are":[47],"hard":[48],"access,":[50],"nor":[51],"directly":[52],"queryable.":[53],"We":[54,68,130,147,155,191],"propose":[55,131],"GVEX,":[56],"a":[57,70,82,88,95,102,109,185,206],"novel":[58],"paradigm":[59],"generates":[61,168],"Graph":[62],"Views":[63],"GNN":[65,145],"EXplanation.":[66],"(1)":[67],"design":[69],"two-tier":[71],"structure":[73],"called":[74],"views.":[76],"An":[77],"view":[79],"consists":[80],"set":[83,89],"patterns":[86],"induced":[91],"subgraphs.":[93],"Given":[94],"database":[96],"G":[97,119],"multiple":[99],"graphs":[100],"label":[105],"l":[106,124],"assigned":[107,126],"by":[108,127],"GNN-based":[110],"classifier":[111],"M,":[112],"it":[113],"concisely":[114],"describes":[115],"fraction":[117],"best":[121,173],"explains":[122],"why":[123],"is":[125,152],"M.":[128],"(2)":[129],"quality":[132,223],"measures":[133],"formulate":[135],"an":[136,163,197,209,221],"optimization":[137],"problem":[138,151],"compute":[140],"optimal":[141],"views":[143],"explanation.":[146],"show":[148,192],"\u03a32P-hard.":[153],"(3)":[154],"present":[156],"two":[157],"algorithms.":[158],"The":[159],"first":[160,167],"one":[161],"follows":[162],"explain-and-summarize":[164],"strategy":[165,195],"high-quality":[169],"subgraphs":[171],"which":[172],"explain":[174],"terms":[177],"feature":[179],"influence":[180],"maximization,":[181],"then":[183],"performs":[184,205],"summarization":[186],"step":[187],"generate":[189],"patterns.":[190],"this":[194],"provides":[196],"approximation":[198],"ratio":[199],"1/2.":[201],"Our":[202],"second":[203],"algorithm":[204],"single-pass":[207],"input":[210],"node":[211],"stream":[212],"batches":[214],"incrementally":[216],"maintain":[217],"views,":[219],"having":[220],"anytime":[222],"guarantee":[224],"1/4-approximation.":[226],"Using":[227],"real-world":[228],"benchmark":[229],"data,":[230],"we":[231,244],"experimentally":[232],"demonstrate":[233],"effectiveness,":[235],"efficiency,":[236],"scalability":[238],"GVEX.":[240,250],"Through":[241],"case":[242],"studies,":[243],"showcase":[245],"practical":[247],"applications":[248]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":5}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}