{"id":"https://openalex.org/W4401863588","doi":"https://doi.org/10.1145/3637528.3671939","title":"An Energy-centric Framework for Category-free Out-of-distribution Node Detection in Graphs","display_name":"An Energy-centric Framework for Category-free Out-of-distribution Node Detection in Graphs","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401863588","doi":"https://doi.org/10.1145/3637528.3671939"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671939","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671939","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/A5103324448","display_name":"Zheng Gong","orcid":"https://orcid.org/0000-0003-1222-4055"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Zheng Gong","raw_affiliation_strings":["Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046835504","display_name":"Ying Sun","orcid":"https://orcid.org/0000-0002-4763-6060"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Ying Sun","raw_affiliation_strings":["Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I200769079"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103324448"],"corresponding_institution_ids":["https://openalex.org/I200769079"],"apc_list":null,"apc_paid":null,"fwci":1.4549,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83161033,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"908","last_page":"919"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9973999857902527,"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"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9973999857902527,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9970999956130981,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9969000220298767,"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/computer-science","display_name":"Computer science","score":0.5990534424781799},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5264756679534912},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.47399699687957764},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1577921211719513},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12125828862190247},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11957710981369019}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5990534424781799},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5264756679534912},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.47399699687957764},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1577921211719513},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12125828862190247},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11957710981369019},{"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:10.1145/3637528.3671939","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671939","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":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8500000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1932198206","https://openalex.org/W2064058256","https://openalex.org/W2116825644","https://openalex.org/W2136891251","https://openalex.org/W2756203131","https://openalex.org/W2808544127","https://openalex.org/W2809583854","https://openalex.org/W2944250323","https://openalex.org/W2965683718","https://openalex.org/W2998496395","https://openalex.org/W3013557831","https://openalex.org/W3068123808","https://openalex.org/W3093664513","https://openalex.org/W3102969158","https://openalex.org/W3103720336","https://openalex.org/W3206326321","https://openalex.org/W4254182148","https://openalex.org/W4290875057","https://openalex.org/W4385768064","https://openalex.org/W4393153621"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Graph":[0],"neural":[1],"networks":[2],"have":[3],"garnered":[4],"notable":[5],"attention":[6],"for":[7,103],"effectively":[8],"processing":[9],"graph-structured":[10],"data.":[11],"Prevalent":[12],"models":[13],"prioritize":[14],"improving":[15],"in-distribution":[16],"(IND)":[17],"data":[18],"performance,":[19],"frequently":[20],"overlooking":[21],"the":[22,36,63,71,74,132],"risks":[23],"from":[24,44,62],"potential":[25],"out-of-distribution":[26],"(OOD)":[27],"nodes":[28,43,72,143,153],"during":[29],"training":[30,162],"and":[31,130,157],"inference.":[32],"In":[33],"real-world":[34,170],"graphs,":[35],"automated":[37],"network":[38],"construction":[39],"can":[40,139],"introduce":[41,115],"noisy":[42],"unknown":[45],"distributions.":[46],"Previous":[47],"research":[48],"into":[49],"OOD":[50,60,85,104,133,142,146,193],"node":[51,105,121,128,147,194],"detection,":[52,106],"typically":[53],"referred":[54,107],"to":[55,108,119],"as":[56,109,125],"entropy-based":[57,84],"methods,":[58],"calculates":[59],"measurements":[61],"prediction":[64],"entropy":[65],"alongside":[66],"category":[67],"classification":[68],"training.":[69],"However,":[70],"in":[73,88,180],"graph":[75],"might":[76],"not":[77],"be":[78],"pre-labeled":[79],"with":[80,144,161],"specific":[81],"categories,":[82],"rendering":[83],"detectors":[86],"inapplicable":[87],"such":[89],"category-free":[90],"situations.":[91],"To":[92],"tackle":[93],"this":[94,112],"issue,":[95],"we":[96,114],"propose":[97],"an":[98,116],"energy-centric":[99],"density":[100,129],"estimation":[101],"framework":[102,175],"EnergyDef.":[110],"Within":[111],"framework,":[113],"energy-based":[117],"GNN":[118],"compute":[120],"energies":[122],"that":[123,173],"act":[124],"indicators":[126],"of":[127,135,182,188],"reveal":[131],"uncertainty":[134],"nodes.":[136],"Importantly,":[137],"EnergyDef":[138],"efficiently":[140],"identify":[141],"low-resource":[145],"annotations,":[148],"achieved":[149],"by":[150],"sampling":[151],"hallucinated":[152],"via":[154],"Langevin":[155],"Dynamics":[156],"structure":[158],"estimation,":[159],"along":[160],"through":[163],"Contrastive":[164],"Divergence.":[165],"Our":[166],"comprehensive":[167],"experiments":[168],"on":[169],"datasets":[171],"substantiate":[172],"our":[174],"markedly":[176],"surpasses":[177],"state-of-the-art":[178],"methods":[179],"terms":[181],"detection":[183],"quality,":[184],"even":[185],"under":[186],"conditions":[187],"scarce":[189],"or":[190],"entirely":[191],"absent":[192],"annotations.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}