{"id":"https://openalex.org/W4401863545","doi":"https://doi.org/10.1145/3637528.3671727","title":"Efficient and Effective Anchored Densest Subgraph Search: A Convex-programming based Approach","display_name":"Efficient and Effective Anchored Densest Subgraph Search: A Convex-programming based Approach","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401863545","doi":"https://doi.org/10.1145/3637528.3671727"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671727","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671727","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5101507959","display_name":"Xiaowei Ye","orcid":"https://orcid.org/0000-0003-0982-341X"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaowei Ye","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100742464","display_name":"Rong-Hua Li","orcid":"https://orcid.org/0009-0003-1153-8906"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong-Hua Li","raw_affiliation_strings":["Key Laboratory of Intelligent Supply Chain Technology, Longgang District, Shenzhen; Beijing Institute of Technology, Beijing, China","Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Supply Chain Technology, Longgang District, Shenzhen; Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106713457","display_name":"Lei Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei Liang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111300990","display_name":"Zhizhen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhizhen Liu","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012547856","display_name":"Longlong Lin","orcid":"https://orcid.org/0000-0002-2194-8146"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Longlong Lin","raw_affiliation_strings":["Southwest University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054991337","display_name":"Guoren Wang","orcid":"https://orcid.org/0000-0002-0181-8379"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoren Wang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101507959"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":1.6875,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.83825937,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3907","last_page":"3918"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9894000291824341,"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"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9894000291824341,"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/T10374","display_name":"Advanced Graph Theory Research","score":0.9835000038146973,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.9825999736785889,"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/computer-science","display_name":"Computer science","score":0.6145243644714355},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.4955517053604126},{"id":"https://openalex.org/keywords/induced-subgraph-isomorphism-problem","display_name":"Induced subgraph isomorphism problem","score":0.4616919755935669},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3289051055908203},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2833189368247986},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.1766018569469452}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6145243644714355},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.4955517053604126},{"id":"https://openalex.org/C191241153","wikidata":"https://www.wikidata.org/wiki/Q6027240","display_name":"Induced subgraph isomorphism problem","level":5,"score":0.4616919755935669},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3289051055908203},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2833189368247986},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.1766018569469452},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.0},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671727","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671727","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W776559747","https://openalex.org/W1898392233","https://openalex.org/W1964419312","https://openalex.org/W1997219719","https://openalex.org/W1998837416","https://openalex.org/W2036836182","https://openalex.org/W2037487875","https://openalex.org/W2126539515","https://openalex.org/W2136486572","https://openalex.org/W2145351510","https://openalex.org/W2164928285","https://openalex.org/W2207622687","https://openalex.org/W2212315060","https://openalex.org/W2517150822","https://openalex.org/W2605272442","https://openalex.org/W2753758798","https://openalex.org/W2771561887","https://openalex.org/W2951294508","https://openalex.org/W2952223573","https://openalex.org/W2962788915","https://openalex.org/W2964129681","https://openalex.org/W2970828623","https://openalex.org/W3046691040","https://openalex.org/W3094534277","https://openalex.org/W3098417430","https://openalex.org/W3163230711","https://openalex.org/W4239035626","https://openalex.org/W4250042253","https://openalex.org/W4281779720","https://openalex.org/W4313172799","https://openalex.org/W4382240020"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W231720905","https://openalex.org/W1482551403","https://openalex.org/W2532922352","https://openalex.org/W2128390795","https://openalex.org/W2915540008","https://openalex.org/W2604893261","https://openalex.org/W3002471523","https://openalex.org/W2390279801"],"abstract_inverted_index":{"The":[0,47,185],"quest":[1],"to":[2,9,62,72,97,133],"identify":[3],"local":[4],"dense":[5,76],"communities":[6,94],"closely":[7],"connected":[8],"predetermined":[10],"seed":[11,20,98],"nodes":[12,21,42,99],"is":[13,30,55,60],"vital":[14],"across":[15],"numerous":[16],"applications.":[17],"Given":[18],"the":[19,23,41,64,109,116,141,195],"R,":[22],"R-subgraph":[24,58],"density":[25,35,87,120],"of":[26,36,118,145,203],"a":[27,84,89,125,136],"subgraph":[28,52,112],"S":[29,37,44],"defined":[31],"as":[32,124],"traditional":[33],"graph":[34,105],"with":[38,210],"penalties":[39],"on":[40,57,181],"in":[43,201],"/":[45],"R.":[46],"state-of-the-art":[48],"(SOTA)":[49],"anchored":[50,110],"densest":[51,111],"model,":[53],"which":[54],"based":[56],"density,":[59],"designed":[61],"address":[63],"community":[65],"search":[66,113],"problem.":[67,129],"However,":[68],"it":[69,123],"often":[70],"struggles":[71],"efficiently":[73],"uncover":[74],"truly":[75],"communities.":[77],"To":[78,150],"eliminate":[79],"this":[80,152],"issue,":[81],"we":[82,155],"propose":[83,156],"novel":[85],"NR-subgraph":[86,119],"metric,":[88],"nuanced":[90],"measure":[91],"that":[92,163,173,188],"identifies":[93],"intimately":[95],"linked":[96],"and":[100,121,143,168],"also":[101,207],"exhibiting":[102],"overall":[103],"high":[104],"density.":[106],"We":[107,177],"redefine":[108],"problem":[114],"through":[115],"lens":[117],"cast":[122],"Linear":[126],"Programming":[127],"(LP)":[128],"This":[130],"allows":[131],"us":[132],"transition":[134],"into":[135,140],"dual":[137],"problem,":[138,154],"tapping":[139],"efficiency":[142],"effectiveness":[144],"convex":[146],"programming-based":[147],"iterative":[148,161],"algorithm.":[149],"solve":[151],"redefined":[153],"two":[157],"algorithms:":[158],"FDP,":[159],"an":[160,170],"method":[162],"swiftly":[164],"attains":[165],"near-optimal":[166],"solutions,":[167],"FDPE,":[169],"exact":[171],"approach":[172],"ensures":[174],"full":[175],"convergence.":[176],"perform":[178],"extensive":[179],"experiments":[180],"12":[182],"real-world":[183],"networks.":[184],"results":[186],"show":[187],"our":[189],"proposed":[190],"algorithms":[191],"not":[192],"only":[193],"outperform":[194],"SOTA":[196],"methods":[197],"by":[198],"3.6~14.1":[199],"times":[200],"terms":[202],"running":[204],"time,":[205],"but":[206],"produce":[208],"subgraphs":[209],"superior":[211],"internal":[212],"quality.":[213]},"counts_by_year":[{"year":2025,"cited_by_count":6}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}