{"id":"https://openalex.org/W7129105631","doi":"https://doi.org/10.1145/3773966.3777968","title":"Multi-view Graph Condensation via Tensor Decomposition","display_name":"Multi-view Graph Condensation via Tensor Decomposition","publication_year":2026,"publication_date":"2026-02-16","ids":{"openalex":"https://openalex.org/W7129105631","doi":"https://doi.org/10.1145/3773966.3777968"},"language":null,"primary_location":{"id":"doi:10.1145/3773966.3777968","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3777968","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3773966.3777968","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123106874","display_name":"N\u00edcolas Roque dos Santos","orcid":null},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nicolas Roque dos Santos","raw_affiliation_strings":["University of California, Riverside, Riverside, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056204499","display_name":"Dawon Ahn","orcid":null},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dawon Ahn","raw_affiliation_strings":["University of California, Riverside, Riverside, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077706568","display_name":"Diego Minatel","orcid":"https://orcid.org/0000-0002-4735-0337"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Diego Minatel","raw_affiliation_strings":["University of S\u00e3o Paulo, S\u00e3o Carlos, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"University of S\u00e3o Paulo, S\u00e3o Carlos, SP, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102303361","display_name":"Alneu de Andrade Lopes","orcid":null},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alneu de Andrade Lopes","raw_affiliation_strings":["University of S\u00e3o Paulo, S\u00e3o Carlos, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"University of S\u00e3o Paulo, S\u00e3o Carlos, SP, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"last","author":{"id":null,"display_name":"Evangelos Papalexakis","orcid":"https://orcid.org/0000-0002-3411-8483"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Evangelos Papalexakis","raw_affiliation_strings":["University of California, Riverside, Riverside, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5123106874"],"corresponding_institution_ids":["https://openalex.org/I103635307"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.48863312,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"563","last_page":"573"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.7809000015258789,"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":0.7809000015258789,"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/T12303","display_name":"Tensor decomposition and applications","score":0.09279999881982803,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.02239999920129776,"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/interpretability","display_name":"Interpretability","score":0.7408999800682068},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46070000529289246},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.46059998869895935},{"id":"https://openalex.org/keywords/graph-bandwidth","display_name":"Graph bandwidth","score":0.3395000100135803},{"id":"https://openalex.org/keywords/graph-database","display_name":"Graph database","score":0.31700000166893005},{"id":"https://openalex.org/keywords/graph-partition","display_name":"Graph partition","score":0.3102000057697296},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.3059000074863434},{"id":"https://openalex.org/keywords/modular-decomposition","display_name":"Modular decomposition","score":0.29499998688697815}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7408999800682068},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6703000068664551},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5738000273704529},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3666999936103821},{"id":"https://openalex.org/C134727501","wikidata":"https://www.wikidata.org/wiki/Q5597073","display_name":"Graph bandwidth","level":5,"score":0.3395000100135803},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.31700000166893005},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.3102000057697296},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.3059000074863434},{"id":"https://openalex.org/C187407849","wikidata":"https://www.wikidata.org/wiki/Q6889712","display_name":"Modular decomposition","level":5,"score":0.29499998688697815},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C112953755","wikidata":"https://www.wikidata.org/wiki/Q739462","display_name":"Graph drawing","level":3,"score":0.2750999927520752},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26899999380111694},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.2678999900817871},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26660001277923584},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.26100000739097595},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.2590000033378601}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3773966.3777968","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3777968","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3773966.3777968","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3777968","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1504886279","https://openalex.org/W1753149633","https://openalex.org/W1997865285","https://openalex.org/W2013029404","https://openalex.org/W2057685268","https://openalex.org/W2068888615","https://openalex.org/W2070135644","https://openalex.org/W2129934604","https://openalex.org/W2154747411","https://openalex.org/W2469230926","https://openalex.org/W2949736877","https://openalex.org/W2954996726","https://openalex.org/W3006631416","https://openalex.org/W3007309629","https://openalex.org/W3035739162","https://openalex.org/W3172620719","https://openalex.org/W3174828871","https://openalex.org/W3194259208","https://openalex.org/W3194651497","https://openalex.org/W4283076909","https://openalex.org/W4296962108","https://openalex.org/W4311079930","https://openalex.org/W4319300193","https://openalex.org/W4385567993","https://openalex.org/W4396758687","https://openalex.org/W4403723664"],"related_works":[],"abstract_inverted_index":{"Graph":[0,50,166],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4,126],"demonstrated":[5],"remarkable":[6],"results":[7],"in":[8,214],"various":[9],"real-world":[10,195],"applications,":[11],"including":[12],"drug":[13],"discovery,":[14],"object":[15],"detection,":[16],"social":[17],"media":[18],"analysis,":[19],"recommender":[20],"systems,":[21],"and":[22,48,107,142,159,185,222],"text":[23],"classification.":[24],"In":[25,117],"contrast":[26],"to":[27,41,58,101,129,149,172,174,210,229],"their":[28,46,84,147],"vast":[29],"potential,":[30],"training":[31],"them":[32],"on":[33,92,193,216,225],"large-scale":[34],"graphs":[35,227],"presents":[36],"significant":[37],"computational":[38],"challenges":[39],"due":[40],"the":[42,70,74,79,111,114],"resources":[43],"required":[44],"for":[45],"storage":[47],"processing.":[49],"Condensation":[51,167],"has":[52],"emerged":[53],"as":[54],"a":[55,64,93,103,120,139,161,211],"promising":[56],"solution":[57],"reduce":[59],"these":[60],"demands":[61],"by":[62],"learning":[63],"synthetic":[65,106],"compact":[66],"graph":[67,87,136,150,184,202],"that":[68,198],"preserves":[69],"essential":[71],"information":[72],"of":[73,113,123,219],"original":[75,108],"one":[76],"while":[77,204],"maintaining":[78],"GNN's":[80],"predictive":[81],"performance.":[82,190],"Despite":[83],"efficacy,":[85],"current":[86],"condensation":[88,151],"approaches":[89],"frequently":[90],"rely":[91],"computationally":[94],"intensive":[95],"bi-level":[96],"optimization.":[97],"Moreover,":[98],"they":[99],"fail":[100],"maintain":[102],"mapping":[104],"between":[105],"nodes,":[109],"limiting":[110],"interpretability":[112],"model's":[115],"decisions.":[116],"this":[118,157],"sense,":[119],"wide":[121],"range":[122],"decomposition":[124],"techniques":[125,178],"been":[127],"applied":[128],"learn":[130],"linear":[131],"or":[132],"multi-linear":[133],"functions":[134],"from":[135],"data,":[137],"offering":[138],"more":[140],"transparent":[141],"less":[143],"resource-intensive":[144],"alternative.":[145],"However,":[146],"applicability":[148],"remains":[152],"unexplored.":[153],"This":[154],"paper":[155],"addresses":[156],"gap":[158],"proposes":[160],"novel":[162],"method":[163],"called":[164],"Multi-view":[165],"via":[168],"Tensor":[169],"Decomposition":[170],"(GCTD)":[171],"investigate":[173],"what":[175],"extent":[176],"such":[177],"can":[179],"synthesize":[180],"an":[181],"informative":[182],"smaller":[183],"achieve":[186],"comparable":[187],"downstream":[188],"task":[189],"Extensive":[191],"experiments":[192],"six":[194,220],"datasets":[196,221],"demonstrate":[197],"GCTD":[199],"effectively":[200],"reduces":[201],"size":[203],"preserving":[205],"GNN":[206],"performance,":[207],"achieving":[208],"up":[209],"4.0%":[212],"improvement":[213],"accuracy":[215],"three":[217],"out":[218],"competitive":[223],"performance":[224],"large":[226],"compared":[228],"existing":[230],"approaches.":[231],"Our":[232],"code":[233],"is":[234],"available":[235],"at":[236],"https://github.com/nicolasrsantos/gctd.":[237]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-02-17T00:00:00"}