{"id":"https://openalex.org/W4401857166","doi":"https://doi.org/10.1145/3637528.3671877","title":"Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling","display_name":"Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401857166","doi":"https://doi.org/10.1145/3637528.3671877"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671877","doi":"https://doi.org/10.1145/3637528.3671877","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671877","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671877","source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671877","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092178835","display_name":"Siwei Zhang","orcid":"https://orcid.org/0009-0008-3994-083X","observed_orcids":["https://orcid.org/0009-0008-3994-083X"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siwei Zhang","raw_affiliation_strings":["Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0008-3994-083X","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030297934","display_name":"Xi Chen","orcid":"https://orcid.org/0009-0003-6156-4811","observed_orcids":["https://orcid.org/0009-0003-6156-4811"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Chen","raw_affiliation_strings":["Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0003-6156-4811","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001877137","display_name":"Yun Xiong","orcid":"https://orcid.org/0000-0002-8575-5415","observed_orcids":["https://orcid.org/0000-0002-8575-5415"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Xiong","raw_affiliation_strings":["Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-8575-5415","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001968036","display_name":"Xixi Wu","orcid":"https://orcid.org/0000-0002-9935-5957","observed_orcids":["https://orcid.org/0000-0002-9935-5957"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xixi Wu","raw_affiliation_strings":["Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-9935-5957","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029845198","display_name":"Yao Zhang","orcid":"https://orcid.org/0000-0003-1481-8826","observed_orcids":["https://orcid.org/0000-0003-1481-8826"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Zhang","raw_affiliation_strings":["Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-1481-8826","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113347808","display_name":"Yongrui Fu","orcid":"https://orcid.org/0009-0001-0309-8264","observed_orcids":["https://orcid.org/0009-0001-0309-8264"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongrui Fu","raw_affiliation_strings":["Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0001-0309-8264","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yinglong Zhao","orcid":"https://orcid.org/0009-0006-4146-6132","observed_orcids":["https://orcid.org/0009-0006-4146-6132"]},"institutions":[{"id":"https://openalex.org/I4405270328","display_name":"Ant Group (China)","ror":"https://ror.org/00g5w7n47","country_code":"CN","type":"company","lineage":["https://openalex.org/I4405270328"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinglong Zhao","raw_affiliation_strings":["Ant Group, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0006-4146-6132","affiliations":[{"raw_affiliation_string":"Ant Group, Shanghai, China","institution_ids":["https://openalex.org/I4405270328"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100462824","display_name":"Jiawei Zhang","orcid":"https://orcid.org/0000-0002-2111-7617","observed_orcids":["https://orcid.org/0000-0002-2111-7617"]},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiawei Zhang","raw_affiliation_strings":["IFM Lab, Department of Computer Science, University of California, Davis, Davis, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-2111-7617","affiliations":[{"raw_affiliation_string":"IFM Lab, Department of Computer Science, University of California, Davis, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8323,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.88471431,"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":"4290","last_page":"4301"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994999766349792,"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":0.9994999766349792,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.00019999999494757503,"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"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/temporal-graph-networks","display_name":"temporal graph networks","score":0.9559999704360962},{"id":"https://openalex.org/keywords/information-decay","display_name":"information decay","score":0.621999979019165},{"id":"https://openalex.org/keywords/time-evolution","display_name":"time evolution","score":0.5770000219345093}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.798280656337738},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6355558037757874},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.5147309303283691},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5093151330947876},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5009398460388184},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.49315208196640015},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46034884452819824},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.45980703830718994},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.344488263130188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3382946252822876},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27977699041366577},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.12261456251144409},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3637528.3671877","doi":"https://doi.org/10.1145/3637528.3671877","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671877","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671877","source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2406.11891","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2406.11891","pdf_url":"https://arxiv.org/pdf/2406.11891","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2406.11891","doi":"https://doi.org/10.48550/arxiv.2406.11891","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2406.11891","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1145/3637528.3671877","doi":"https://doi.org/10.1145/3637528.3671877","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671877","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671877","source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3540205131","display_name":null,"funder_award_id":"22dz1200704","funder_id":"https://openalex.org/F4320313610","funder_display_name":"Shanghai Science and Technology Development Foundation"},{"id":"https://openalex.org/G3615559023","display_name":"III: Medium: Collaborative Research: Self-Supervised Recommender System Learning with Application Specific Adaption","funder_award_id":"2106972","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3800173056","display_name":"III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation","funder_award_id":"1763365","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5340839507","display_name":null,"funder_award_id":"U1936213","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320313610","display_name":"Shanghai Science and Technology Development Foundation","ror":"https://ror.org/030enq015"},{"id":"https://openalex.org/F4320318398","display_name":"Ant Group","ror":"https://ror.org/00g5w7n47"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401857166.pdf","grobid_xml":"https://content.openalex.org/works/W4401857166.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W2242818861","https://openalex.org/W2749590927","https://openalex.org/W2897818259","https://openalex.org/W2963415211","https://openalex.org/W2965683718","https://openalex.org/W2995533131","https://openalex.org/W3036685317","https://openalex.org/W3043308157","https://openalex.org/W3048692209","https://openalex.org/W3080510905","https://openalex.org/W3122137342","https://openalex.org/W3195040486","https://openalex.org/W3210512903","https://openalex.org/W3211394146","https://openalex.org/W3211399938","https://openalex.org/W3214495341","https://openalex.org/W4221164465","https://openalex.org/W4256361765","https://openalex.org/W4281555548","https://openalex.org/W4312206464","https://openalex.org/W4321853807","https://openalex.org/W4361194049","https://openalex.org/W4382202753","https://openalex.org/W4382469817","https://openalex.org/W4385567983","https://openalex.org/W4386271038","https://openalex.org/W4387724174","https://openalex.org/W4387846295","https://openalex.org/W4387846408","https://openalex.org/W4390872585","https://openalex.org/W4391766677","https://openalex.org/W7133195610","https://openalex.org/W7133231348","https://openalex.org/W7133243892"],"related_works":["https://openalex.org/W7160637073","https://openalex.org/W4226336107","https://openalex.org/W7207605573","https://openalex.org/W7133206535","https://openalex.org/W4312620662","https://openalex.org/W7129605985","https://openalex.org/W4362681502","https://openalex.org/W4404388945","https://openalex.org/W4294791395","https://openalex.org/W4405026464"],"abstract_inverted_index":{"Temporal":[0],"Graph":[1],"Networks":[2],"(TGNs)":[3],"have":[4],"demonstrated":[5],"their":[6,38,103,210],"remarkable":[7],"performance":[8,211,233,240],"in":[9,222],"modeling":[10],"temporal":[11,18,61],"interaction":[12],"graphs.":[13],"These":[14],"works":[15],"can":[16,56,94],"generate":[17],"node":[19],"representations":[20],"by":[21,75,170,202],"encoding":[22,79,113],"the":[23,27,47,110,131,139,144,173,180,190],"surrounding":[24,156],"neighborhoods":[25],"for":[26,43,49,86,143],"target":[28,145],"node.":[29,146],"However,":[30],"an":[31,50],"inherent":[32],"limitation":[33],"of":[34,152,175],"existing":[35,73,99],"TGNs":[36,74],"is":[37],"reliance":[39],"onfixed,":[40],"hand-crafted":[41],"rules":[42],"neighborhood":[44,54,78,112,125,168],"encoding,":[45],"overlooking":[46],"necessity":[48],"adaptive":[51,111],"and":[52,60,122,178,193,216,241],"learnable":[53],"that":[55,93,228],"accommodate":[57],"both":[58,189],"personalization":[59],"evolution":[62],"across":[63,235],"different":[64],"timestamps.":[65],"In":[66],"this":[67,223],"paper,":[68],"we":[69,108,129,161],"aim":[70],"to":[71,186,232],"enhance":[72],"introducing":[76],"anadaptive":[77],"mechanism.":[80],"We":[81,198],"present":[82],"SEAN":[83,204,229],"(Selective":[84],"Encoding":[85],"Adaptive":[87],"Neighborhood),":[88],"a":[89,149,163],"flexible":[90],"plug-and-play":[91],"model":[92,185],"be":[95],"seamlessly":[96],"integrated":[97],"with":[98],"TGNs,":[100,208],"effectively":[101],"boosting":[102],"performance.":[104],"To":[105],"achieve":[106],"this,":[107],"decompose":[109],"process":[114],"into":[115,205],"two":[116],"phases:":[117],"(i)":[118],"representative":[119,207],"neighbor":[120],"selection,":[121],"(ii)":[123],"temporal-aware":[124],"information":[126,195],"aggregation.":[127,197],"Specifically,":[128],"propose":[130,162],"Representative":[132],"Neighbor":[133],"Selector":[134],"component,":[135],"which":[136,166],"automatically":[137],"pinpoints":[138],"most":[140],"important":[141],"neighbors":[142],"It":[147],"offers":[148],"tailored":[150],"understanding":[151],"each":[153],"node's":[154],"unique":[155],"context,":[157],"facilitating":[158],"personalization.":[159],"Subsequently,":[160],"Temporal-aware":[164],"Aggregator,":[165],"synthesizes":[167],"aggregation":[169,176],"selectively":[171],"determining":[172],"utilization":[174],"routes":[177],"decaying":[179],"outdated":[181],"information,":[182],"allowing":[183],"our":[184],"adaptively":[187],"leverage":[188],"contextually":[191],"significant":[192],"current":[194],"during":[196],"conduct":[199],"extensive":[200],"experiments":[201],"integrating":[203],"three":[206],"evaluating":[209],"on":[212],"four":[213],"public":[214],"datasets":[215],"one":[217],"financial":[218],"benchmark":[219],"dataset":[220],"introduced":[221],"paper.":[224],"The":[225],"results":[226],"demonstrate":[227],"consistently":[230],"leads":[231],"improvements":[234],"all":[236],"models,":[237],"achieving":[238],"SOTA":[239],"exceptional":[242],"robustness.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2024-08-25T00:00:00"}
