{"id":"https://openalex.org/W4409158315","doi":"https://doi.org/10.1145/3690624.3709215","title":"N <scp>ode</scp> I <scp>mport</scp> : Imbalanced Node Classification with Node Importance Assessment","display_name":"N <scp>ode</scp> I <scp>mport</scp> : Imbalanced Node Classification with Node Importance Assessment","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409158315","doi":"https://doi.org/10.1145/3690624.3709215"},"language":"en","primary_location":{"id":"doi:10.1145/3690624.3709215","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3690624.3709215","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 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3690624.3709215","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012362464","display_name":"N.A. Chen","orcid":"https://orcid.org/0009-0000-6225-5111"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nan Chen","raw_affiliation_strings":["Johns Hopkins University, Baltimore, Maryland, USA"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University, Baltimore, Maryland, USA","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030809906","display_name":"Zemin Liu","orcid":"https://orcid.org/0000-0001-6262-9435"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zemin Liu","raw_affiliation_strings":["Zhejiang University, Hangzhou, Zhejiang, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065675832","display_name":"Bryan Hooi","orcid":"https://orcid.org/0000-0002-5645-1754"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Bryan Hooi","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039946576","display_name":"Bingsheng He","orcid":"https://orcid.org/0000-0001-8618-4581"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Bingsheng He","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101766936","display_name":"Jun Hu","orcid":"https://orcid.org/0000-0003-1277-6802"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jun Hu","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114400769","display_name":"Jia Chen","orcid":"https://orcid.org/0009-0003-1174-0063"},"institutions":[{"id":"https://openalex.org/I4210146998","display_name":"MOH Holdings","ror":"https://ror.org/052jm1735","country_code":"SG","type":"healthcare","lineage":["https://openalex.org/I4210146998"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jia Chen","raw_affiliation_strings":["Grabtaxi Holdings Pte Ltd, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Grabtaxi Holdings Pte Ltd, Singapore, Singapore","institution_ids":["https://openalex.org/I4210146998"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5012362464"],"corresponding_institution_ids":["https://openalex.org/I145311948"],"apc_list":null,"apc_paid":null,"fwci":2.8599,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90180079,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"94","last_page":"105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.998199999332428,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.998199999332428,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9925000071525574,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9847000241279602,"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/node","display_name":"Node (physics)","score":0.7644520998001099},{"id":"https://openalex.org/keywords/ode","display_name":"Ode","score":0.5807115435600281},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5465020537376404},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3518759310245514},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20451483130455017},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14445257186889648},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.05791515111923218}],"concepts":[{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.7644520998001099},{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.5807115435600281},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5465020537376404},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3518759310245514},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20451483130455017},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14445257186889648},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.05791515111923218},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3690624.3709215","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3690624.3709215","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 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3690624.3709215","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3690624.3709215","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 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2118978333","https://openalex.org/W2148143831","https://openalex.org/W2907492528","https://openalex.org/W2963224980","https://openalex.org/W2963351448","https://openalex.org/W2963691377","https://openalex.org/W3035286001","https://openalex.org/W3035552357","https://openalex.org/W3113682753","https://openalex.org/W3134509497","https://openalex.org/W3153858161","https://openalex.org/W3158299003","https://openalex.org/W3171389163","https://openalex.org/W3174366534","https://openalex.org/W3177200443","https://openalex.org/W3207949751","https://openalex.org/W4224326479","https://openalex.org/W4292948016","https://openalex.org/W4366352743","https://openalex.org/W4382239870","https://openalex.org/W6600062020","https://openalex.org/W6745136726"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2371448224","https://openalex.org/W3134495997","https://openalex.org/W2363680170","https://openalex.org/W2376428685","https://openalex.org/W4256082577","https://openalex.org/W625783435","https://openalex.org/W2048742619"],"abstract_inverted_index":{"In":[0],"real-world":[1],"applications,":[2],"node":[3,80,93,127,141,152,191,202,264],"classification":[4,81,265],"on":[5,53],"graphs":[6],"often":[7,29,63],"faces":[8],"the":[9,130,189,195,218,250,255],"challenge":[10],"of":[11,66,182,188,225,254],"class":[12,54,114,259],"imbalance,":[13,260],"where":[14,90],"majority":[15,40],"classes":[16,41],"dominate":[17],"training,":[18,121],"resulting":[19],"in":[20,31,175,257],"biased":[21],"model":[22,100,120,173],"performance.":[23],"Traditional":[24],"Graph":[25],"Neural":[26],"Networks":[27],"(GNNs)":[28],"struggle":[30],"such":[32],"scenarios,":[33],"as":[34],"they":[35],"tend":[36],"to":[37,39,78,138,210,262],"overfit":[38],"while":[42],"underrepresenting":[43],"minority":[44,61],"classes.":[45],"Existing":[46],"solutions,":[47],"which":[48],"either":[49],"prioritize":[50],"nodes":[51,59,110,170],"based":[52],"size":[55],"or":[56],"synthesize":[57],"new":[58],"for":[60,87,119,123,151],"classes,":[62],"fall":[64],"short":[65],"effectively":[67],"addressing":[68],"this":[69,156,183],"imbalance":[70,115],"issue.":[71],"This":[72],"paper":[73],"introduces":[74],"a":[75,84,91,136,160,208,212],"novel":[76,161],"approach":[77],"class-imbalanced":[79],"by":[82,155],"utilizing":[83],"balanced":[85],"meta-set":[86,214],"importance":[88],"measurement,":[89],"training":[92,131],"is":[94,185,270],"considered":[95],"significant":[96],"if":[97],"it":[98],"enhances":[99],"performance":[101,174],"under":[102],"an":[103,148,176],"unbiased":[104,177],"setting.":[105],"Our":[106,247],"method":[107],"identifies":[108],"important":[109],"that":[111,163,171,215],"can":[112],"counteract":[113],"and":[116,125,146,168,252],"utilizes":[117],"them":[118],"allowing":[122],"fine-grained":[124],"dynamic":[126],"selection":[128],"throughout":[129],"process.":[132],"We":[133,228],"theoretically":[134],"derive":[135],"formula":[137],"directly":[139],"assess":[140],"importance,":[142],"reducing":[143],"computational":[144],"overhead":[145],"providing":[147],"intuitive":[149],"threshold":[150],"selection.":[153],"Guided":[154],"metric,":[157],"we":[158,206],"develop":[159],"framework":[162,184,256],"filters":[164],"valuable":[165],"labeled,":[166],"unlabeled,":[167],"synthetic":[169,190],"enhance":[172],"context.":[178],"A":[179],"key":[180],"advantage":[181],"its":[186,242],"separation":[187],"generation":[192,203],"process":[193],"from":[194],"filtering":[196],"process,":[197],"ensuring":[198,222],"compatibility":[199],"with":[200],"various":[201],"techniques.":[204],"Furthermore,":[205],"introduce":[207],"strategy":[209],"construct":[211],"high-quality":[213],"closely":[216],"approximates":[217],"overall":[219],"feature":[220],"distribution,":[221],"robust":[223],"representation":[224],"each":[226],"class.":[227],"evaluate":[229],"our":[230],"framework,":[231],"NodeImport,":[232],"across":[233],"multiple":[234],"benchmark":[235],"datasets":[236],"using":[237],"popular":[238],"GNN":[239],"architectures,":[240],"demonstrating":[241],"superiority":[243],"over":[244],"state-of-the-art":[245],"baselines.":[246],"results":[248],"highlight":[249],"flexibility":[251],"effectiveness":[253],"mitigating":[258],"leading":[261],"improved":[263],"outcomes.":[266],"The":[267],"source":[268],"code":[269],"available":[271],"at":[272],"https://github.com/NanChanNN/NodeImport.":[273]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}