{"id":"https://openalex.org/W7137369538","doi":"https://doi.org/10.48550/arxiv.2603.12275","title":"GONE: Structural Knowledge Unlearning via Neighborhood-Expanded Distribution Shaping","display_name":"GONE: Structural Knowledge Unlearning via Neighborhood-Expanded Distribution Shaping","publication_year":2026,"publication_date":"2026-02-21","ids":{"openalex":"https://openalex.org/W7137369538","doi":"https://doi.org/10.48550/arxiv.2603.12275"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.12275","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12275","pdf_url":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.12275","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129421254","display_name":"Chahana Dahal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dahal, Chahana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114043407","display_name":"A. Balasubramaniam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balasubramaniam, Ashutosh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5054098183","display_name":"Zuobin Xiong","orcid":"https://orcid.org/0000-0002-6562-9825"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Zuobin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9294999837875366,"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":0.9294999837875366,"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":0.03189999982714653,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.008200000040233135,"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/leverage","display_name":"Leverage (statistics)","score":0.5260999798774719},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5171999931335449},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.39750000834465027},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3968999981880188},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.36730000376701355},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.32100000977516174},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.3066999912261963}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7013000249862671},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5260999798774719},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5171999931335449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4909999966621399},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.39750000834465027},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33180001378059387},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32690000534057617},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.28769999742507935},{"id":"https://openalex.org/C14156362","wikidata":"https://www.wikidata.org/wiki/Q3235388","display_name":"Descriptive knowledge","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.27559998631477356},{"id":"https://openalex.org/C2986065213","wikidata":"https://www.wikidata.org/wiki/Q743861","display_name":"Implicit knowledge","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C2778584943","wikidata":"https://www.wikidata.org/wiki/Q6459541","display_name":"LOOM","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C56949724","wikidata":"https://www.wikidata.org/wiki/Q219079","display_name":"Truth table","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C30038468","wikidata":"https://www.wikidata.org/wiki/Q4354775","display_name":"Memorization","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.12275","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12275","pdf_url":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.12275","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12275","pdf_url":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7763527035713196,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Unlearning":[0],"knowledge":[1,60,83,87,153],"is":[2,122,176],"a":[3,79,118,134],"pressing":[4],"and":[5,20,33,43,58,75,109,128,142,149,155,166,172],"challenging":[6],"task":[7],"in":[8,61,91],"Large":[9],"Language":[10],"Models":[11],"(LLMs)":[12],"because":[13],"of":[14,99,102],"their":[15],"unprecedented":[16],"capability":[17],"to":[18,67,124],"memorize":[19],"digest":[21],"training":[22],"data":[23,52],"at":[24,178],"scale,":[25],"raising":[26],"more":[27],"significant":[28],"issues":[29],"regarding":[30],"safety,":[31],"privacy,":[32],"intellectual":[34],"property.":[35],"However,":[36],"existing":[37],"works,":[38],"including":[39],"parameter":[40],"editing,":[41],"fine-tuning,":[42],"distillation-based":[44],"methods,":[45],"are":[46],"all":[47],"focused":[48],"on":[49,147,163,168,170],"flat":[50],"sentence-level":[51],"but":[53],"overlook":[54],"the":[55,97,139],"relational,":[56],"multi-hop,":[57],"reasoned":[59],"naturally":[62],"structured":[63,86],"data.":[64],"In":[65,112],"response":[66],"this":[68,70],"gap,":[69],"paper":[71],"introduces":[72],"Graph":[73],"Oblivion":[74],"Node":[76],"Erasure":[77],"(GONE),":[78],"benchmark":[80,95],"for":[81],"evaluating":[82],"unlearning":[84,120,156,164],"over":[85],"graph":[88,126],"(KG)":[89],"facts":[90],"LLMs.":[92],"This":[93],"KG-based":[94],"enables":[96],"disentanglement":[98],"three":[100],"effects":[101],"unlearning:":[103],"direct":[104],"fact":[105,141],"removal,":[106],"reasoning-based":[107],"leakage,":[108],"catastrophic":[110],"forgetting.":[111],"addition,":[113],"Neighborhood-Expanded":[114],"Distribution":[115],"Shaping":[116],"(NEDS),":[117],"novel":[119],"framework,":[121],"designed":[123],"leverage":[125],"connectivity":[127],"identify":[129],"anchor":[130],"correlated":[131],"neighbors,":[132],"enforcing":[133],"precise":[135],"decision":[136],"boundary":[137],"between":[138],"forgotten":[140],"its":[143],"semantic":[144],"neighborhood.":[145],"Evaluations":[146],"LLaMA-3-8B":[148],"Mistral-7B":[150],"across":[151],"multiple":[152],"editing":[154],"methods":[157],"showcase":[158],"NEDS's":[159],"superior":[160],"performance":[161],"(1.000":[162],"efficacy":[165],"0.839":[167],"locality)":[169],"GONE":[171],"other":[173],"benchmarks.":[174],"Code":[175],"available":[177],"https://anonymous.4open.science/r/GONE-4679/.":[179]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-17T00:00:00"}