{"id":"https://openalex.org/W7140209583","doi":"https://doi.org/10.48550/arxiv.2603.20829","title":"Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering","display_name":"Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering","publication_year":2026,"publication_date":"2026-03-21","ids":{"openalex":"https://openalex.org/W7140209583","doi":"https://doi.org/10.48550/arxiv.2603.20829"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20829","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.2603.20829","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Liu, Yunhui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yunhui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Yue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Yongchao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yongchao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zheng, Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Li, Stan Z.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Stan Z.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Xinwang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xinwang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"He, Tieke","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Tieke","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":0.9143000245094299,"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":0.9143000245094299,"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":0.04600000008940697,"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":0.005100000184029341,"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/cluster-analysis","display_name":"Cluster analysis","score":0.5945000052452087},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.578499972820282},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.460999995470047},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4388999938964844},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4027999937534332},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4000000059604645},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.35089999437332153},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.35010001063346863}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7488999962806702},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5945000052452087},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.578499972820282},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4819999933242798},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47510001063346863},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4388999938964844},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40540000796318054},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4027999937534332},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4000000059604645},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.352400004863739},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.35089999437332153},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28049999475479126},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.25200000405311584},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20829","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.2603.20829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20829","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":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5786540508270264}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Attributed":[0],"Graph":[1],"Clustering":[2],"(AGC)":[3],"is":[4],"a":[5,35,40,63,124,154,210],"fundamental":[6],"unsupervised":[7,140,163,223],"task":[8],"that":[9,158],"partitions":[10],"nodes":[11],"into":[12,87],"cohesive":[13],"groups":[14],"by":[15],"jointly":[16],"modeling":[17],"structural":[18,164],"topology":[19],"and":[20,30,48,96,106,142,166,190,222],"node":[21],"attributes.":[22],"While":[23],"the":[24,49,77,83,120,132,143,174],"advent":[25],"of":[26,37,52,68,134,146,177],"graph":[27],"neural":[28],"networks":[29],"self-supervised":[31],"learning":[32],"has":[33],"catalyzed":[34],"proliferation":[36],"AGC":[38,69],"methodologies,":[39],"widening":[41],"chasm":[42],"persists":[43],"between":[44],"academic":[45,122],"benchmark":[46],"performance":[47],"stringent":[50],"demands":[51],"real-world":[53],"industrial":[54,178],"deployment.":[55,179],"To":[56],"bridge":[57],"this":[58,60],"gap,":[59],"survey":[61],"provides":[62],"comprehensive,":[64],"industrially":[65],"grounded":[66],"review":[67],"from":[70,198],"three":[71,88],"complementary":[72],"perspectives.":[73],"First,":[74],"we":[75,112,151,171,202,208],"introduce":[76],"Encode-Cluster-Optimize":[78],"taxonomic":[79],"framework,":[80],"which":[81],"decomposes":[82],"diverse":[84],"algorithmic":[85],"landscape":[86],"orthogonal,":[89],"composable":[90],"modules:":[91],"representation":[92],"encoding,":[93],"cluster":[94],"projection,":[95],"optimization":[97],"strategy.":[98],"This":[99],"unified":[100],"paradigm":[101],"enables":[102],"principled":[103],"architectural":[104],"comparisons":[105],"inspires":[107],"novel":[108],"methodological":[109],"combinations.":[110],"Second,":[111],"critically":[113],"examine":[114],"prevailing":[115],"evaluation":[116,156],"protocols":[117],"to":[118,227],"expose":[119],"field's":[121],"monoculture:":[123],"pervasive":[125],"over-reliance":[126],"on":[127],"small,":[128],"homophilous":[129],"citation":[130],"networks,":[131],"inadequacy":[133],"supervised-only":[135],"metrics":[136],"for":[137,153,213],"an":[138],"inherently":[139],"task,":[141],"chronic":[144],"neglect":[145],"computational":[147],"scalability.":[148],"In":[149],"response,":[150],"advocate":[152],"holistic":[155],"standard":[157],"integrates":[159],"supervised":[160],"semantic":[161],"alignment,":[162],"integrity,":[165],"rigorous":[167],"efficiency":[168],"profiling.":[169],"Third,":[170],"explicitly":[172],"confront":[173],"practical":[175],"realities":[176],"By":[180],"analyzing":[181],"operational":[182],"constraints":[183],"such":[184],"as":[185],"massive":[186],"scale,":[187],"severe":[188],"heterophily,":[189],"tabular":[191],"feature":[192],"noise":[193],"alongside":[194],"extensive":[195],"empirical":[196],"evidence":[197],"our":[199],"companion":[200],"benchmark,":[201],"outline":[203],"actionable":[204],"engineering":[205],"strategies.":[206],"Furthermore,":[207],"chart":[209],"clear":[211],"roadmap":[212],"future":[214],"research,":[215],"prioritizing":[216],"heterophily-robust":[217],"encoders,":[218],"scalable":[219],"joint":[220],"optimization,":[221],"model":[224],"selection":[225],"criteria":[226],"meet":[228],"production-grade":[229],"requirements.":[230]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-25T00:00:00"}
