{"id":"https://openalex.org/W7164831533","doi":"https://doi.org/10.4230/lipics.sea.2026.25","title":"Engineering Learned Heuristics to Improve Clustering for Multilevel Graph Partitioning","display_name":"Engineering Learned Heuristics to Improve Clustering for Multilevel Graph Partitioning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7164831533","doi":"https://doi.org/10.4230/lipics.sea.2026.25"},"language":"en","primary_location":{"id":"pmh:doi:10.4230/lipics.sea.2026.25","is_oa":true,"landing_page_url":"https://zenodo.org/records/19387774","pdf_url":"http://jmlr.org/papers/volume23/21-0644/21-0644.pdf","source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"ConferencePaper"},"type":"conference-paper","indexed_in":["datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://jmlr.org/papers/volume23/21-0644/21-0644.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132827262","display_name":"Simeon Schrape","orcid":null},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Schrape, Simeon","raw_affiliation_strings":["Karlsruhe Institute of Technology, Germany"],"raw_orcid":"https://orcid.org/0009-0004-9073-5330","affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047490630","display_name":"Nikolai Maas","orcid":"https://orcid.org/0009-0002-6959-417X"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Maas, Nikolai","raw_affiliation_strings":["Karlsruhe Institute of Technology, Germany"],"raw_orcid":"https://orcid.org/0009-0002-6959-417X","affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115426694","display_name":"Kenneth Langedal","orcid":"https://orcid.org/0009-0001-6838-4640"},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Langedal, Kenneth","raw_affiliation_strings":["Faculty of Mathematics and Computer Science, Heidelberg University, Germany"],"raw_orcid":"https://orcid.org/0009-0001-6838-4640","affiliations":[{"raw_affiliation_string":"Faculty of Mathematics and Computer Science, Heidelberg University, Germany","institution_ids":["https://openalex.org/I223822909"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034585164","display_name":"Daniel Seemaier","orcid":"https://orcid.org/0000-0002-1997-1304"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Seemaier, Daniel","raw_affiliation_strings":["Karlsruhe Institute of Technology, Germany"],"raw_orcid":"https://orcid.org/0000-0002-1997-1304","affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.70584361,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.888700008392334,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.888700008392334,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.0215000007301569,"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.010400000028312206,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/cluster-analysis","display_name":"Cluster analysis","score":0.6309000253677368},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6079999804496765},{"id":"https://openalex.org/keywords/graph-partition","display_name":"Graph partition","score":0.5852000117301941},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.5569000244140625},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.492000013589859},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42660000920295715},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.41670000553131104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.751800000667572},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6309000253677368},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6079999804496765},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.5852000117301941},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.5569000244140625},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.492000013589859},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46320000290870667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4447000026702881},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.3752000033855438},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3456000089645386},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3246000111103058},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.27239999175071716},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.2531999945640564}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:doi:10.4230/lipics.sea.2026.25","is_oa":true,"landing_page_url":"https://zenodo.org/records/19387774","pdf_url":"http://jmlr.org/papers/volume23/21-0644/21-0644.pdf","source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"ConferencePaper"},{"id":"pmh:oai:drops-oai.dagstuhl.de:26029","is_oa":true,"landing_page_url":"https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2026.25","pdf_url":"https://drops.dagstuhl.de/storage/00lipics/lipics-vol371-sea2026/LIPIcs.SEA.2026.25/LIPIcs.SEA.2026.25.pdf","source":{"id":"https://openalex.org/S4377196569","display_name":"DROPS (Schloss Dagstuhl \u2013 Leibniz Center for Informatics)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2799853480","host_organization_name":"Schloss Dagstuhl \u2013 Leibniz Center for Informatics","host_organization_lineage":["https://openalex.org/I2799853480"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"InProceedings"},{"id":"doi:10.4230/lipics.sea.2026.25","is_oa":true,"landing_page_url":"https://doi.org/10.4230/lipics.sea.2026.25","pdf_url":null,"source":{"id":"https://openalex.org/S4393917817","display_name":"Leibniz international proceedings in informatics","issn_l":"1868-8969","issn":["1868-8969"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"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":"ConferencePaper"}],"best_oa_location":{"id":"pmh:doi:10.4230/lipics.sea.2026.25","is_oa":true,"landing_page_url":"https://zenodo.org/records/19387774","pdf_url":"http://jmlr.org/papers/volume23/21-0644/21-0644.pdf","source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"ConferencePaper"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1079818582","display_name":null,"funder_award_id":"AC02\u201005CH11231","funder_id":"https://openalex.org/F4320338292","funder_display_name":"Lawrence Berkeley National Laboratory"},{"id":"https://openalex.org/G1143651532","display_name":null,"funder_award_id":"DE-AC02-05CH11231","funder_id":"https://openalex.org/F4320338292","funder_display_name":"Lawrence Berkeley National Laboratory"},{"id":"https://openalex.org/G1286236842","display_name":null,"funder_award_id":"-AC02-05CH11231","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G3083819904","display_name":null,"funder_award_id":"05CH11231","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G42916606","display_name":null,"funder_award_id":"DE AC02 05CH11231","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G7354628648","display_name":null,"funder_award_id":"05CH11231","funder_id":"https://openalex.org/F4320338292","funder_display_name":"Lawrence Berkeley National Laboratory"},{"id":"https://openalex.org/G969889393","display_name":null,"funder_award_id":"DE-AC02-","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"}],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320338292","display_name":"Lawrence Berkeley National Laboratory","ror":"https://ror.org/02jbv0t02"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7164831533.pdf","grobid_xml":"https://content.openalex.org/works/W7164831533.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Balanced":[0],"Graph":[1],"Partitioning":[2],"is":[3],"a":[4,51,65,84,90,158,177],"classical":[5],"optimization":[6],"problem":[7,25],"where":[8],"quality":[9,131,153,167],"guarantees":[10],"are":[11,185],"computationally":[12],"infeasible,":[13],"and":[14,117,136,165],"practical":[15,62],"solvers":[16],"therefore":[17],"rely":[18,33],"on":[19,35,145,157,168],"manually":[20],"engineered":[21],"heuristics.":[22],"Yet,":[23],"the":[24,78,101,105,114,122,128,134,139,182,189],"has":[26],"also":[27],"proven":[28],"difficult":[29],"for":[30,92],"approaches":[31],"that":[32,69,184],"heavily":[34],"machine":[36,71],"learning":[37,72],"-":[38],"especially":[39],"since":[40],"applications":[41],"often":[42],"need":[43],"to":[44,60,73,88,113,176],"partition":[45],"graphs":[46,149,161,190],"of":[47,54,104,121,138,155,160,179],"huge":[48],"scale":[49],"in":[50,100],"short":[52],"amount":[53],"time.":[55],"Instead,":[56],"we":[57,126],"demonstrate":[58],"how":[59],"achieve":[61],"improvements":[63,174],"with":[64,110,162],"more":[66,146],"careful":[67],"approach":[68],"uses":[70],"improve":[74,127],"heuristic":[75],"decisions":[76,99],"within":[77],"state-of-the-art":[79],"solver":[80],"Mt-KaHyPar.":[81],"We":[82],"use":[83],"pre-trained":[85],"neural":[86,123],"network":[87,124],"predict":[89],"score":[91],"each":[93],"edge,":[94],"which":[95],"then":[96],"guides":[97],"clustering":[98,115],"first":[102],"phase":[103],"partitioning":[106],"(the":[107],"coarsening).":[108],"Combined":[109],"corresponding":[111],"adjustments":[112],"algorithm":[116],"an":[118,151],"efficient":[119],"implementation":[120],"logic,":[125],"overall":[129],"solution":[130],"while":[132],"preserving":[133],"efficiency":[135],"scalability":[137],"original":[140],"algorithm.":[141],"Our":[142],"detailed":[143],"evaluation":[144],"than":[147,188],"180":[148],"shows":[150],"average":[152],"improvement":[154],"2%":[156],"class":[159],"beneficial":[163],"properties,":[164],"unchanged":[166],"all":[169],"remaining":[170],"graphs.":[171],"Moreover,":[172],"our":[173],"generalize":[175],"set":[178],"instances":[180],"from":[181],"literature":[183],"much":[186],"larger":[187],"used":[191],"during":[192],"training.":[193]},"counts_by_year":[],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2026-06-16T00:00:00"}