{"id":"https://openalex.org/W4416510389","doi":"https://doi.org/10.1137/25m1772393","title":"Conductance Estimation in Digraphs: Submodular Transformation, Lov\u00e1sz Extension, and Dinkelbach Iteration","display_name":"Conductance Estimation in Digraphs: Submodular Transformation, Lov\u00e1sz Extension, and Dinkelbach Iteration","publication_year":2026,"publication_date":"2026-07-10","ids":{"openalex":"https://openalex.org/W4416510389","doi":"https://doi.org/10.1137/25m1772393"},"language":"en","primary_location":{"id":"doi:10.1137/25m1772393","is_oa":false,"landing_page_url":"https://doi.org/10.1137/25m1772393","pdf_url":null,"source":{"id":"https://openalex.org/S16958353","display_name":"SIAM Journal on Matrix Analysis and Applications","issn_l":"0895-4798","issn":["0895-4798","1095-7162"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Matrix Analysis and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2506.23131","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061728488","display_name":"Sihong Shao","orcid":"https://orcid.org/0000-0001-7439-9163"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sihong Shao","raw_affiliation_strings":["CAPT, LMAM and School of Mathematical Sciences, Peking University, Beijing 100871, China"],"raw_orcid":"https://orcid.org/0000-0001-7439-9163","affiliations":[{"raw_affiliation_string":"CAPT, LMAM and School of Mathematical Sciences, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111734166","display_name":"Chuan Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Yang","raw_affiliation_strings":["School of Mathematics and Statistics, Fuzhou University, Fuzhou 350108, China, and School of Mathematical Sciences, Peking University, Beijing 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Fuzhou University, Fuzhou 350108, China, and School of Mathematical Sciences, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570","https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103178154","display_name":"Xiaoyun Ye","orcid":"https://orcid.org/0000-0002-1395-4060"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyang Ye","raw_affiliation_strings":["School of Mathematical Sciences, Peking University, Beijing 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]}],"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":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01756334,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"47","issue":"3","first_page":"1166","last_page":"1185"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.6944000124931335,"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":0.6944000124931335,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.12229999899864197,"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"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.08330000191926956,"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/digraph","display_name":"Digraph","score":0.9038000106811523},{"id":"https://openalex.org/keywords/submodular-set-function","display_name":"Submodular set function","score":0.5375000238418579},{"id":"https://openalex.org/keywords/directed-graph","display_name":"Directed graph","score":0.4925999939441681},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.4726000130176544},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.4586000144481659},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4401000142097473},{"id":"https://openalex.org/keywords/extension","display_name":"Extension (predicate logic)","score":0.4074000120162964},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.39259999990463257},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.3813000023365021}],"concepts":[{"id":"https://openalex.org/C2779145032","wikidata":"https://www.wikidata.org/wiki/Q191494","display_name":"Digraph","level":2,"score":0.9038000106811523},{"id":"https://openalex.org/C178621042","wikidata":"https://www.wikidata.org/wiki/Q7631710","display_name":"Submodular set function","level":2,"score":0.5375000238418579},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5239999890327454},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.49230000376701355},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.4726000130176544},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.4586000144481659},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4221000075340271},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.4074000120162964},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.39259999990463257},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.38109999895095825},{"id":"https://openalex.org/C64999384","wikidata":"https://www.wikidata.org/wiki/Q3507967","display_name":"Symmetrization","level":2,"score":0.3499000072479248},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3465999960899353},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C38754835","wikidata":"https://www.wikidata.org/wiki/Q2003238","display_name":"Strongly connected component","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C148764684","wikidata":"https://www.wikidata.org/wiki/Q621751","display_name":"Approximation algorithm","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C64331007","wikidata":"https://www.wikidata.org/wiki/Q831672","display_name":"Spanning tree","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C121932024","wikidata":"https://www.wikidata.org/wiki/Q5159376","display_name":"Conductance","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C3018234147","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Undirected graph","level":3,"score":0.2574999928474426},{"id":"https://openalex.org/C3018263672","wikidata":"https://www.wikidata.org/wiki/Q1296251","display_name":"Efficient algorithm","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1137/25m1772393","is_oa":false,"landing_page_url":"https://doi.org/10.1137/25m1772393","pdf_url":null,"source":{"id":"https://openalex.org/S16958353","display_name":"SIAM Journal on Matrix Analysis and Applications","issn_l":"0895-4798","issn":["0895-4798","1095-7162"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Matrix Analysis and Applications","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2506.23131","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.23131","pdf_url":"https://arxiv.org/pdf/2506.23131","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2506.23131","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.23131","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2506.23131","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.23131","pdf_url":"https://arxiv.org/pdf/2506.23131","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2519258023","display_name":null,"funder_award_id":"12288101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2565695553","display_name":null,"funder_award_id":"12526521","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4214708025","display_name":null,"funder_award_id":"12325112","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Abstract.":[0],"Conventional":[1],"spectral":[2],"digraph":[3,59,91,121,165],"partitioning":[4,16],"methods":[5,163],"typically":[6],"symmetrize":[7],"the":[8,13,36,46,56,80,86,111,133],"adjacency":[9],"matrix,":[10],"thereby":[11],"transforming":[12],"directed":[14,51],"graph":[15,29],"problem":[17,89],"into":[18],"an":[19,96],"undirected":[20],"one,":[21],"where":[22],"bipartitioning":[23],"is":[24,129,135],"commonly":[25],"linked":[26],"to":[27,44,50,71,137,140],"minimizing":[28],"conductance.":[30,122],"However,":[31],"such":[32],"symmetrization":[33],"approaches":[34],"disregard":[35],"directional":[37],"dependencies":[38],"of":[39,79],"edges":[40],"in":[41,90,164],"digraphs,":[42],"failing":[43],"capture":[45],"inherent":[47],"imbalance":[48],"crucial":[49],"network":[52],"modeling.":[53],"Building":[54],"on":[55,147],"parallels":[57],"between":[58],"conductance":[60,62,166],"and":[61,109,132,149],"under":[63],"submodular":[64],"transformations,":[65],"we":[66],"develop":[67],"a":[68,104,141],"generalized":[69],"framework":[70,84],"derive":[72],"their":[73],"continuous":[74],"formulations.":[75],"By":[76],"leveraging":[77],"properties":[78],"Lov\u00e1sz":[81],"extension,":[82],"this":[83],"addresses":[85],"fundamental":[87],"asymmetry":[88],"partitioning.":[92],"We":[93],"then":[94],"formulate":[95],"equivalent":[97],"fractional":[98],"programming":[99],"problem,":[100],"relax":[101],"it":[102],"via":[103],"three-step":[105],"Dinkelbach":[106],"iteration":[107],"procedure,":[108],"design":[110],"Directed":[112],"Simple":[113],"Iterative":[114],"([Formula:":[115],"see":[116,127,156],"text])":[117],"algorithm":[118,134,158],"for":[119],"estimating":[120],"The":[123],"subproblem":[124],"within":[125],"[Formula:":[126,155],"text]":[128,157],"analytically":[130],"solvable,":[131],"guaranteed":[136],"converge":[138],"provably":[139],"binary":[142],"local":[143],"optimum.":[144],"Extensive":[145],"experiments":[146],"synthetic":[148],"real-world":[150],"networks":[151],"demonstrate":[152],"that":[153],"our":[154],"significantly":[159],"outperforms":[160],"several":[161],"state-of-the-art":[162],"minimization.":[167]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}