{"id":"https://openalex.org/W3089930297","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207639","title":"GCN-LRP explanation: exploring latent attention of graph convolutional networks","display_name":"GCN-LRP explanation: exploring latent attention of graph convolutional networks","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089930297","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207639","mag":"3089930297"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207639","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207639","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5042282783","display_name":"Jinlong Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinlong Hu","raw_affiliation_strings":["Guangdong Key Laboratory of Communication & Computer Network, South China University of China, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Communication & Computer Network, South China University of China, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101530601","display_name":"Tenghui Li","orcid":"https://orcid.org/0000-0002-8810-962X"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tenghui Li","raw_affiliation_strings":["Guangdong Key Laboratory of Communication & Computer Network, South China University of China, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Communication & Computer Network, South China University of China, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052760299","display_name":"Shoubin Dong","orcid":"https://orcid.org/0000-0003-0153-850X"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shoubin Dong","raw_affiliation_strings":["Guangdong Key Laboratory of Communication & Computer Network, South China University of China, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Communication & Computer Network, South China University of China, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5042282783"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.548,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.73998933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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":0.9997000098228455,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9987000226974487,"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/T10028","display_name":"Topic Modeling","score":0.9915000200271606,"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":0.71225506067276},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.638990581035614},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5939515829086304},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5067117810249329},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44851449131965637},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44112324714660645},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32755887508392334},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07341116666793823}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.71225506067276},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.638990581035614},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5939515829086304},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5067117810249329},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44851449131965637},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44112324714660645},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32755887508392334},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07341116666793823},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"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:10.1109/ijcnn48605.2020.9207639","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207639","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7200000286102295,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1662382123","https://openalex.org/W1677991150","https://openalex.org/W1787224781","https://openalex.org/W2101491865","https://openalex.org/W2153959628","https://openalex.org/W2162341153","https://openalex.org/W2163605009","https://openalex.org/W2290847742","https://openalex.org/W2295107390","https://openalex.org/W2513671774","https://openalex.org/W2624431344","https://openalex.org/W2745165948","https://openalex.org/W2776207810","https://openalex.org/W2789186812","https://openalex.org/W2792839479","https://openalex.org/W2804057010","https://openalex.org/W2887630004","https://openalex.org/W2945827377","https://openalex.org/W2950898568","https://openalex.org/W2962711740","https://openalex.org/W2962767366","https://openalex.org/W2962851944","https://openalex.org/W2962858109","https://openalex.org/W2963241951","https://openalex.org/W2963272802","https://openalex.org/W2963374347","https://openalex.org/W2963711743","https://openalex.org/W2963858333","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2964114465","https://openalex.org/W2964311892","https://openalex.org/W2964321699","https://openalex.org/W2965469401","https://openalex.org/W2973136764","https://openalex.org/W2979481854","https://openalex.org/W3100157108","https://openalex.org/W3101553402","https://openalex.org/W3137695714","https://openalex.org/W4289700143","https://openalex.org/W4293355908","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4300537282","https://openalex.org/W6637178625","https://openalex.org/W6637311423","https://openalex.org/W6684191040","https://openalex.org/W6685133223","https://openalex.org/W6690815549","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6740373800","https://openalex.org/W6742575306","https://openalex.org/W6745537798","https://openalex.org/W6748141922","https://openalex.org/W6749077313","https://openalex.org/W6752110883","https://openalex.org/W6753782550","https://openalex.org/W6754929296","https://openalex.org/W6763485134"],"related_works":["https://openalex.org/W2085384747","https://openalex.org/W2088166309","https://openalex.org/W1891216533","https://openalex.org/W2106071040","https://openalex.org/W4312133475","https://openalex.org/W4238976562","https://openalex.org/W2276587472","https://openalex.org/W4248323080","https://openalex.org/W2615795876","https://openalex.org/W2049612369"],"abstract_inverted_index":{"Graph":[0],"convolutional":[1],"networks":[2],"(GCNs)":[3],"have":[4],"been":[5],"successfully":[6],"applied":[7],"to":[8,50,99,114],"many":[9],"graph":[10,68,198],"data":[11,64,69,199],"on":[12],"various":[13],"learning":[14,200],"tasks":[15,74],"such":[16],"as":[17],"node":[18,72,89,102],"classification.":[19],"However,":[20],"there":[21],"is":[22],"limited":[23],"understanding":[24],"of":[25,32,55,147,185,194],"the":[26,52,88,100,116,139,144,150,168,181,189],"internal":[27],"logic":[28],"and":[29,66,78,90,120,135,183,192],"decision":[30],"patterns":[31],"GCNs.":[33,56,202],"In":[34],"this":[35],"paper,":[36],"we":[37,58,131],"propose":[38],"a":[39,121],"layer-wise":[40],"relevance":[41],"propagation":[42],"based":[43],"explanation":[44],"method":[45],"for":[46,71,197],"GCNs,":[47],"namely":[48],"GCN-LRP,":[49],"explore":[51],"latent":[53,81,140,169,190],"pattern":[54],"Then,":[57],"use":[59],"three":[60],"well-known":[61],"citation":[62],"network":[63],"sets":[65,70],"synthetic":[67],"classification":[73],"with":[75,105,157,201],"GCN-LRP":[76],"explanation,":[77],"experimentally":[79],"identify":[80],"attentions":[82,141],"when":[83,103],"GCNs":[84,95,109,186],"aggregates":[85],"information":[86],"from":[87,143],"its":[91,106],"neighboring":[92,117,123,151],"nodes:":[93],"(i)":[94,138],"pay":[96,112],"more":[97,126,162],"attention":[98,113,127,163,170],"classified":[101,158],"comparing":[104],"neighbors;":[107,166],"(ii)":[108,149],"do":[110],"not":[111],"all":[115],"nodes":[118,124],"equally,":[119],"few":[122],"received":[125],"than":[128,164],"others.":[129],"Moreover,":[130],"further":[132],"theoretically":[133],"analyze":[134],"find":[136],"that:":[137],"come":[142],"recursively":[145],"aggregating":[146],"GCNs;":[148],"nodes,":[152],"which":[153],"share":[154],"enough":[155],"neighbors":[156],"node,":[159],"would":[160],"receive":[161],"other":[165],"(iii)":[167],"could":[171],"hardly":[172],"be":[173],"changed":[174],"by":[175,188],"model":[176],"training.":[177],"We":[178],"also":[179],"discuss":[180],"advantage":[182],"limitations":[184],"introduced":[187],"attentions,":[191],"implications":[193],"our":[195],"findings":[196]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}