{"id":"https://openalex.org/W7152641744","doi":"https://doi.org/10.48550/arxiv.2604.06596","title":"DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning","display_name":"DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7152641744","doi":"https://doi.org/10.48550/arxiv.2604.06596"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.06596","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06596","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.06596","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133252442","display_name":"S M Shovan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shovan, S M","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133255326","display_name":"Arindam Khanda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khanda, Arindam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133289949","display_name":"S M Ferdous","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ferdous, S M","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079106413","display_name":"Sajal Das","orcid":"https://orcid.org/0000-0002-9641-2864"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Das, Sajal K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5099282353","display_name":"Mahantesh Halappanavar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Halappanavar, Mahantesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.35409998893737793,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.35409998893737793,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.3257000148296356,"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/T10057","display_name":"Face and Expression Recognition","score":0.0406000018119812,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.7261000275611877},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5748000144958496},{"id":"https://openalex.org/keywords/fraction","display_name":"Fraction (chemistry)","score":0.42829999327659607},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.41589999198913574},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.329800009727478},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3285999894142151},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.32190001010894775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7581999897956848},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.7261000275611877},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5748000144958496},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4636000096797943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43689998984336853},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.41589999198913574},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4009000062942505},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3158000111579895},{"id":"https://openalex.org/C172658912","wikidata":"https://www.wikidata.org/wiki/Q661613","display_name":"Batch processing","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2720000147819519},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2680000066757202},{"id":"https://openalex.org/C90806461","wikidata":"https://www.wikidata.org/wiki/Q1144416","display_name":"Propagation delay","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C3018263672","wikidata":"https://www.wikidata.org/wiki/Q1296251","display_name":"Efficient algorithm","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C109659709","wikidata":"https://www.wikidata.org/wiki/Q3407504","display_name":"Affinity propagation","level":5,"score":0.2556999921798706},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.06596","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06596","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.06596","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06596","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":{"Semi-supervised":[0],"learning":[1],"aims":[2],"to":[3,26,54,95,122],"infer":[4],"class":[5],"labels":[6,28,57],"using":[7],"only":[8,89],"a":[9,44,78],"small":[10],"fraction":[11],"of":[12,29,68],"labeled":[13],"data.":[14],"In":[15],"graph-based":[16],"semi-supervised":[17],"learning,":[18],"this":[19],"is":[20,58],"typically":[21],"achieved":[22],"through":[23],"label":[24,51,71],"propagation":[25,52,72],"predict":[27],"unlabeled":[30],"nodes.":[31],"However,":[32],"in":[33,40],"real-world":[34],"applications,":[35],"data":[36],"often":[37],"arrive":[38],"incrementally":[39],"batches.":[41],"Each":[42],"time":[43],"new":[45],"batch":[46],"appears,":[47],"reapplying":[48],"the":[49,66,90,96],"traditional":[50],"algorithm":[53,86,109],"recompute":[55],"all":[56],"redundant,":[59],"computationally":[60],"intensive,":[61],"and":[62,114],"inefficient.":[63],"To":[64],"address":[65],"absence":[67],"an":[69],"efficient":[70],"update":[73],"method,":[74],"we":[75],"propose":[76],"DynLP,":[77],"novel":[79],"GPU-centric":[80],"Dynamic":[81],"Batched":[82],"Parallel":[83],"Label":[84],"Propagation":[85],"that":[87],"performs":[88],"necessary":[91],"updates,":[92],"propagating":[93],"changes":[94],"relevant":[97],"subgraph":[98],"without":[99],"requiring":[100],"full":[101],"recalculation.":[102],"By":[103],"exploiting":[104],"GPU":[105],"architectural":[106],"optimizations,":[107],"our":[108],"achieves":[110],"on":[111,118],"average":[112],"13x":[113],"upto":[115],"102x":[116],"speedup":[117],"large-scale":[119],"datasets":[120],"compared":[121],"state-of-the-art":[123],"approaches.":[124]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-10T00:00:00"}