{"id":"https://openalex.org/W7148579370","doi":"https://doi.org/10.1109/hipcw66559.2025.00069","title":"Graph Coarsening for High-Performance GNN","display_name":"Graph Coarsening for High-Performance GNN","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7148579370","doi":"https://doi.org/10.1109/hipcw66559.2025.00069"},"language":null,"primary_location":{"id":"doi:10.1109/hipcw66559.2025.00069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hipcw66559.2025.00069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 32nd International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132782334","display_name":"Niharika Nayak","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121466","display_name":"Indian Institute of Technology Bhilai","ror":"https://ror.org/02sscsx71","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210121466"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Niharika Nayak","raw_affiliation_strings":["IIT Bhilai,Department of CSE,India"],"affiliations":[{"raw_affiliation_string":"IIT Bhilai,Department of CSE,India","institution_ids":["https://openalex.org/I4210121466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132735872","display_name":"Kishan Tamboli","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121466","display_name":"Indian Institute of Technology Bhilai","ror":"https://ror.org/02sscsx71","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210121466"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kishan Tamboli","raw_affiliation_strings":["IIT Bhilai,Department of CSE,India"],"affiliations":[{"raw_affiliation_string":"IIT Bhilai,Department of CSE,India","institution_ids":["https://openalex.org/I4210121466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132793485","display_name":"Vishwesh Jatala","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121466","display_name":"Indian Institute of Technology Bhilai","ror":"https://ror.org/02sscsx71","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210121466"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vishwesh Jatala","raw_affiliation_strings":["IIT Bhilai,Department of CSE,India"],"affiliations":[{"raw_affiliation_string":"IIT Bhilai,Department of CSE,India","institution_ids":["https://openalex.org/I4210121466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5132782334"],"corresponding_institution_ids":["https://openalex.org/I4210121466"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87579716,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"229","last_page":"230"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.7601000070571899,"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":0.7601000070571899,"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":0.12309999763965607,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.017799999564886093,"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/graph","display_name":"Graph","score":0.560699999332428},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5587999820709229},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.44350001215934753},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.3856000006198883},{"id":"https://openalex.org/keywords/graph-reduction","display_name":"Graph reduction","score":0.3407000005245209},{"id":"https://openalex.org/keywords/strength-of-a-graph","display_name":"Strength of a graph","score":0.3271999955177307}],"concepts":[{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.560699999332428},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5587999820709229},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.492900013923645},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45260000228881836},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.44350001215934753},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4350999891757965},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3864000141620636},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3856000006198883},{"id":"https://openalex.org/C97042676","wikidata":"https://www.wikidata.org/wiki/Q5597097","display_name":"Graph reduction","level":3,"score":0.3407000005245209},{"id":"https://openalex.org/C19332903","wikidata":"https://www.wikidata.org/wiki/Q7623247","display_name":"Strength of a graph","level":5,"score":0.3271999955177307},{"id":"https://openalex.org/C36038622","wikidata":"https://www.wikidata.org/wiki/Q5428703","display_name":"Factor-critical graph","level":5,"score":0.31790000200271606},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C168291704","wikidata":"https://www.wikidata.org/wiki/Q902252","display_name":"Complement graph","level":5,"score":0.3034999966621399},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.3018999993801117},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.28870001435279846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C198414033","wikidata":"https://www.wikidata.org/wiki/Q5155607","display_name":"Comparability graph","level":5,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hipcw66559.2025.00069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hipcw66559.2025.00069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 32nd International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2070232376","https://openalex.org/W2962936633","https://openalex.org/W3177399989"],"related_works":[],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"are":[4,119],"powerful":[5],"in":[6,36,147,158],"solving":[7],"complex":[8],"problems":[9],"dealing":[10,41],"with":[11,42,45,121],"graph-structured":[12],"data.":[13],"Recently,":[14],"GNN":[15,30,73,136,161],"research":[16],"has":[17],"been":[18],"significantly":[19],"advanced":[20],"due":[21],"to":[22,62,172],"its":[23],"wide":[24],"applicability":[25],"across":[26],"various":[27,135],"domains.":[28],"However,":[29],"training":[31,162],"faces":[32],"significant":[33,145],"scalability":[34],"challenges":[35],"real-world":[37],"graphs,":[38],"especially":[39],"when":[40,170],"large":[43],"graphs":[44],"millions":[46],"of":[47,100,130,166],"nodes":[48],"and":[49,70,97],"edges.To":[50],"address":[51],"these":[52],"challenges,":[53],"we":[54,78,104],"propose":[55],"a":[56,122,144],"graph":[57,64,88,92,108,148],"coarsening":[58,132,156],"technique":[59],"that":[60,84,118,140,155],"aims":[61],"reduce":[63],"size":[65],"while":[66],"preserving":[67],"essential":[68],"structure":[69],"semantics":[71],"for":[72,95],"training.":[74],"To":[75],"achieve":[76],"this,":[77],"first":[79],"compute":[80],"an":[81,164],"edge-weighted":[82],"scheme":[83],"constructs":[85],"the":[86,90,101,106,116,128,134,160,173],"weighted":[87,107],"from":[89],"unweighted":[91],"by":[93,163],"accounting":[94],"spectral":[96],"feature":[98],"similarities":[99],"nodes.":[102],"Subsequently,":[103],"coarsen":[105],"using":[109,133],"Low":[110],"Edge":[111],"Matching":[112],"(LEM),":[113],"which":[114],"coarsens":[115],"vertices":[117],"connected":[120],"low":[123],"edge":[124],"weight.":[125],"We":[126,138],"evaluated":[127],"performance":[129],"our":[131,141,152],"datasets.":[137],"observe":[139],"approach":[142],"shows":[143],"reduction":[146],"size.":[149],"In":[150],"addition,":[151],"results":[153],"show":[154],"helps":[157],"reducing":[159],"average":[165],"2\u00d7":[167],"speed":[168],"up,":[169],"compared":[171],"state-of-the-art":[174],"implementations.":[175]},"counts_by_year":[],"updated_date":"2026-04-03T16:44:17.987007","created_date":"2026-04-03T00:00:00"}