{"id":"https://openalex.org/W4402722495","doi":"https://doi.org/10.48550/arxiv.2408.05459","title":"A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation","display_name":"A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation","publication_year":2024,"publication_date":"2024-08-10","ids":{"openalex":"https://openalex.org/W4402722495","doi":"https://doi.org/10.48550/arxiv.2408.05459"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2408.05459","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.05459","pdf_url":"https://arxiv.org/pdf/2408.05459","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2408.05459","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101911940","display_name":"Yiran Li","orcid":"https://orcid.org/0000-0002-4361-4796"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Yiran","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113395202","display_name":"Gongyao Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Gongyao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007796732","display_name":"J. Y. Shi","orcid":"https://orcid.org/0000-0002-0465-1551"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Jieming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040420455","display_name":"Renchi Yang","orcid":"https://orcid.org/0000-0002-7284-3096"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Renchi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101919635","display_name":"Shiqi Shen","orcid":"https://orcid.org/0009-0002-5442-2121"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Shiqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100438883","display_name":"Qin Li","orcid":"https://orcid.org/0000-0001-6393-3236"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5080199054","display_name":"Jun Luo","orcid":"https://orcid.org/0000-0002-8235-2338"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Jun","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101911940"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9627000093460083,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9627000093460083,"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":0.9363999962806702,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9054999947547913,"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/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.7579507827758789},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6545416116714478},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5180783271789551},{"id":"https://openalex.org/keywords/nearest-neighbor-chain-algorithm","display_name":"Nearest-neighbor chain algorithm","score":0.4937151372432709},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4215964674949646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2750943899154663},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.10886028409004211},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.05639106035232544}],"concepts":[{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.7579507827758789},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6545416116714478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5180783271789551},{"id":"https://openalex.org/C102164700","wikidata":"https://www.wikidata.org/wiki/Q17162702","display_name":"Nearest-neighbor chain algorithm","level":5,"score":0.4937151372432709},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4215964674949646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2750943899154663},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.10886028409004211},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.05639106035232544}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2408.05459","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.05459","pdf_url":"https://arxiv.org/pdf/2408.05459","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2408.05459","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2408.05459","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-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2408.05459","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.05459","pdf_url":"https://arxiv.org/pdf/2408.05459","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402722495.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2519241726","https://openalex.org/W2375128115","https://openalex.org/W2362413895","https://openalex.org/W2368415703","https://openalex.org/W2391642054","https://openalex.org/W2797906284","https://openalex.org/W2182477562","https://openalex.org/W2162583212","https://openalex.org/W1671890395","https://openalex.org/W2169618946"],"abstract_inverted_index":{"Attributed":[0],"networks":[1],"containing":[2],"entity-specific":[3],"information":[4,127],"in":[5,10],"node":[6,42],"attributes":[7,87],"are":[8,61,71,156],"ubiquitous":[9],"modeling":[11],"social":[12],"networks,":[13,163],"e-commerce,":[14],"bioinformatics,":[15],"etc.":[16],"Their":[17],"inherent":[18],"network":[19,52,173],"topology":[20],"ranges":[21],"from":[22],"simple":[23],"graphs":[24,32],"to":[25,45,80,108,136,158,200,208],"hypergraphs":[26],"with":[27,33,146,193,212],"high-order":[28],"interactions":[29],"and":[30,64,74,142,164,187,225,239],"multiplex":[31,183,231],"separate":[34],"layers.":[35],"An":[36],"important":[37],"graph":[38,179,184],"mining":[39],"task":[40],"is":[41,77],"clustering,":[43],"aiming":[44],"partition":[46],"the":[47,122,150,235],"nodes":[48,60,70,85],"of":[49,94,125,161,177,241],"an":[50,105,138,143],"attributed":[51,95,109,162,172,178,182,217,223,230],"into":[53],"k":[54],"disjoint":[55],"clusters":[56],"such":[57],"that":[58],"intra-cluster":[59],"closely":[62],"connected":[63],"share":[65],"similar":[66],"attributes,":[67],"while":[68],"inter-cluster":[69],"far":[72],"apart":[73],"dissimilar.":[75],"It":[76],"highly":[78],"challenging":[79],"capture":[81],"multi-hop":[82],"connections":[83],"via":[84],"or":[86],"for":[88,121,149,197],"effective":[89,139],"clustering":[90,111,174,180,185,237],"on":[91,128,215,221,228],"multiple":[92],"types":[93,160],"networks.":[96],"In":[97],"this":[98],"paper,":[99],"we":[100,166,190],"first":[101],"present":[102],"AHCKA":[103,113],"as":[104,169],"efficient":[106,144],"approach":[107],"hypergraph":[110,132],"(AHC).":[112],"includes":[114],"a":[115,130,170],"carefully-crafted":[116],"K-nearest":[117],"neighbor":[118],"augmentation":[119],"strategy":[120],"optimized":[123],"exploitation":[124],"attribute":[126],"hypergraphs,":[129,218],"joint":[131],"random":[133],"walk":[134],"model":[135],"devise":[137,191],"AHC":[140],"objective,":[141],"solver":[145],"speedup":[147],"techniques":[148,155],"objective":[151],"optimization.":[152],"The":[153],"proposed":[154],"extensible":[157],"various":[159],"thus,":[165],"develop":[167],"ANCKA":[168,192],"versatile":[171],"framework,":[175],"capable":[176],"(AGC),":[181],"(AMGC),":[186],"AHC.":[188],"Moreover,":[189],"algorithmic":[194],"designs":[195],"tailored":[196],"GPU":[198],"acceleration":[199],"boost":[201],"efficiency.":[202],"We":[203],"have":[204],"conducted":[205],"extensive":[206],"experiments":[207],"compare":[209],"our":[210,242],"methods":[211],"19":[213],"competitors":[214,220,227],"8":[216],"16":[219,226],"6":[222],"graphs,":[224,232],"3":[229],"all":[233],"demonstrating":[234],"superb":[236],"quality":[238],"efficiency":[240],"methods.":[243]},"counts_by_year":[],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2025-10-10T00:00:00"}