{"id":"https://openalex.org/W7124157938","doi":"https://doi.org/10.48550/arxiv.2601.08230","title":"GADPN: Graph Adaptive Denoising and Perturbation Networks via Singular Value Decomposition","display_name":"GADPN: Graph Adaptive Denoising and Perturbation Networks via Singular Value Decomposition","publication_year":2026,"publication_date":"2026-01-13","ids":{"openalex":"https://openalex.org/W7124157938","doi":"https://doi.org/10.48550/arxiv.2601.08230"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.08230","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08230","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.08230","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123018291","display_name":"Hao Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Deng, Hao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123044475","display_name":"Bo Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Bo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5123018291"],"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":0.9790999889373779,"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":0.9790999889373779,"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":0.0026000000070780516,"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":0.0017999999690800905,"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/graph","display_name":"Graph","score":0.5537999868392944},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.4650999903678894},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.40639999508857727},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.3946000039577484},{"id":"https://openalex.org/keywords/homophily","display_name":"Homophily","score":0.390500009059906},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3508000075817108},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.3179999887943268},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.30880001187324524},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.3084999918937683}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5770000219345093},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5537999868392944},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4165000021457672},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40639999508857727},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39750000834465027},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C2779812341","wikidata":"https://www.wikidata.org/wiki/Q5891525","display_name":"Homophily","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.34529998898506165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34139999747276306},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.3034999966621399},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.299699991941452},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2806999981403351},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C2988224531","wikidata":"https://www.wikidata.org/wiki/Q20830730","display_name":"Network structure","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C7948225","wikidata":"https://www.wikidata.org/wiki/Q4723998","display_name":"Algebraic connectivity","level":4,"score":0.2619999945163727},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.08230","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08230","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.08230","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08230","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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"Graph":[1],"Neural":[2],"Networks":[3],"(GNNs)":[4],"excel":[5],"on":[6,146,164],"graph-structured":[7],"data,":[8],"their":[9,66],"performance":[10,154],"is":[11],"fundamentally":[12],"limited":[13],"by":[14],"the":[15,18,109,114,125],"quality":[16],"of":[17],"observed":[19],"graph,":[20],"which":[21],"often":[22,51],"contains":[23],"noise,":[24],"missing":[25],"links,":[26],"or":[27],"structural":[28,93,126],"properties":[29],"misaligned":[30],"with":[31],"GNNs'":[32],"underlying":[33],"assumptions.":[34],"To":[35],"address":[36],"this,":[37],"graph":[38,79,86,175],"structure":[39,80],"learning":[40,81],"aims":[41],"to":[42,57,106,116,129,141,171],"infer":[43],"a":[44,75],"more":[45],"optimal":[46,110],"topology.":[47],"Existing":[48],"methods,":[49],"however,":[50],"incur":[52],"high":[53],"computational":[54],"costs":[55],"due":[56],"complex":[58],"generative":[59],"models":[60],"and":[61,91,121],"iterative":[62],"joint":[63],"optimization,":[64],"limiting":[65],"practical":[67],"utility.":[68],"In":[69],"this":[70],"paper,":[71],"we":[72,102,123],"propose":[73],"GADPN,":[74],"simple":[76],"yet":[77],"effective":[78],"framework":[82],"that":[83,150],"adaptively":[84,107],"refines":[85],"topology":[87],"via":[88,132],"low-rank":[89],"denoising":[90,111],"generalized":[92],"perturbation.":[94],"Our":[95],"approach":[96],"makes":[97],"two":[98],"key":[99],"contributions:":[100],"(1)":[101],"introduce":[103],"Bayesian":[104],"optimization":[105],"determine":[108],"strength,":[112],"tailoring":[113],"process":[115],"each":[117],"graph's":[118],"homophily":[119],"level;":[120],"(2)":[122],"extend":[124],"perturbation":[127],"method":[128],"arbitrary":[130],"graphs":[131],"Singular":[133],"Value":[134],"Decomposition":[135],"(SVD),":[136],"overcoming":[137],"its":[138,169],"original":[139],"limitation":[140],"symmetric":[142],"structures.":[143],"Extensive":[144],"experiments":[145],"benchmark":[147],"datasets":[148],"demonstrate":[149],"GADPN":[151],"achieves":[152],"state-of-the-art":[153],"while":[155],"significantly":[156],"improving":[157],"efficiency.":[158],"It":[159],"shows":[160],"particularly":[161],"strong":[162],"gains":[163],"challenging":[165],"disassortative":[166],"graphs,":[167],"validating":[168],"ability":[170],"robustly":[172],"learn":[173],"enhanced":[174],"structures":[176],"across":[177],"diverse":[178],"network":[179],"types.":[180]},"counts_by_year":[],"updated_date":"2026-01-15T23:21:31.212559","created_date":"2026-01-15T00:00:00"}