{"id":"https://openalex.org/W4403724158","doi":"https://doi.org/10.1109/dsaa61799.2024.10722771","title":"Adversarial Robustness in Graph Neural Networks: Recent Advances and New Frontier","display_name":"Adversarial Robustness in Graph Neural Networks: Recent Advances and New Frontier","publication_year":2024,"publication_date":"2024-10-06","ids":{"openalex":"https://openalex.org/W4403724158","doi":"https://doi.org/10.1109/dsaa61799.2024.10722771"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa61799.2024.10722771","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa61799.2024.10722771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5077764271","display_name":"Zhichao Hou","orcid":"https://orcid.org/0000-0002-3989-2654"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhichao Hou","raw_affiliation_strings":["North Carolina State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North Carolina State University","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039804358","display_name":"Minhua Lin","orcid":"https://orcid.org/0000-0003-1591-7172"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minhua Lin","raw_affiliation_strings":["Pennsylvania State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014426855","display_name":"MohamadAli Torkamani","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"MohamadAli Torkamani","raw_affiliation_strings":["Amazon AWS"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon AWS","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011048500","display_name":"Suhang Wang","orcid":"https://orcid.org/0000-0003-3448-4878"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suhang Wang","raw_affiliation_strings":["Pennsylvania State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100621795","display_name":"Xiaorui Liu","orcid":"https://orcid.org/0000-0001-8217-5688"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaorui Liu","raw_affiliation_strings":["North Carolina State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North Carolina State University","institution_ids":["https://openalex.org/I137902535"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9879000186920166,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9879000186920166,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9211999773979187,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6944459676742554},{"id":"https://openalex.org/keywords/frontier","display_name":"Frontier","score":0.6764047145843506},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6514078974723816},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5795477628707886},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47637078166007996},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4129948019981384},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39261311292648315},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3829675018787384}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6944459676742554},{"id":"https://openalex.org/C2778571376","wikidata":"https://www.wikidata.org/wiki/Q1355821","display_name":"Frontier","level":2,"score":0.6764047145843506},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6514078974723816},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5795477628707886},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47637078166007996},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4129948019981384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39261311292648315},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3829675018787384},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa61799.2024.10722771","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa61799.2024.10722771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2803831897","https://openalex.org/W2949208225","https://openalex.org/W2964015378","https://openalex.org/W2964583308","https://openalex.org/W2964971928","https://openalex.org/W2994506732","https://openalex.org/W2998122931","https://openalex.org/W3035237749","https://openalex.org/W3081203761","https://openalex.org/W3098276446","https://openalex.org/W3117928012","https://openalex.org/W3170068709","https://openalex.org/W3171412022","https://openalex.org/W3172780638","https://openalex.org/W4224298369","https://openalex.org/W4286893581","https://openalex.org/W4289389616","https://openalex.org/W4297733535","https://openalex.org/W4318908127","https://openalex.org/W4367047184","https://openalex.org/W4379538763","https://openalex.org/W4387561063","https://openalex.org/W4389115479","https://openalex.org/W6745537798","https://openalex.org/W6751569023","https://openalex.org/W6780093097","https://openalex.org/W6791426817","https://openalex.org/W6796513057","https://openalex.org/W6803361077","https://openalex.org/W6809840454","https://openalex.org/W6810623460","https://openalex.org/W6810640740","https://openalex.org/W6841058540","https://openalex.org/W6849348130","https://openalex.org/W6857932679"],"related_works":["https://openalex.org/W2347401120","https://openalex.org/W2041961361","https://openalex.org/W2310010941","https://openalex.org/W1988132375","https://openalex.org/W2334292868","https://openalex.org/W579144800","https://openalex.org/W2147233680","https://openalex.org/W2069525434","https://openalex.org/W2502115930","https://openalex.org/W2046798653"],"abstract_inverted_index":{"In":[0,54],"recent":[1],"years,":[2],"Graph":[3],"Neural":[4],"Networks":[5],"(GNNs)":[6],"have":[7],"attracted":[8],"substantial":[9],"attention":[10],"due":[11],"to":[12,41,47,116,120],"their":[13,48],"powerful":[14],"ability":[15],"in":[16,51,71,124],"modeling":[17],"graph-structured":[18],"data":[19],"and":[20,32,67,84,90,109,122],"broad":[21],"applications":[22],"across":[23],"various":[24],"domains":[25],"such":[26],"as":[27],"social":[28],"media,":[29],"biology,":[30],"health":[31],"finance.":[33],"Despite":[34],"these":[35],"successes,":[36],"GNNs":[37],"exhibit":[38],"significant":[39],"vulnerabilities":[40],"adversarial":[42,65],"attacks,":[43],"which":[44],"poses":[45],"challenges":[46],"reliable":[49],"deployment":[50],"real":[52],"scenarios.":[53],"this":[55,125],"tutorial,":[56],"we":[57,103],"will":[58,78,104],"provide":[59],"an":[60],"in-depth":[61],"exploration":[62],"of":[63,75,92],"existing":[64],"attacks":[66],"the":[68,73,107],"state-of-the-art":[69],"techniques":[70],"enhancing":[72],"robustness":[74],"GNNs.":[76],"Participants":[77],"gain":[79],"insights":[80],"into":[81],"advancing":[82],"attack":[83],"defense":[85],"methods,":[86],"along":[87],"with":[88],"evaluation":[89],"comparison":[91],"robust":[93],"GNN":[94],"models.":[95],"Besides":[96],"a":[97],"thorough":[98],"overview":[99],"on":[100,111],"current":[101],"landscape,":[102],"also":[105],"cover":[106],"summary":[108],"discussion":[110],"potential":[112],"future":[113],"directions,":[114],"aiming":[115],"inspire":[117],"more":[118],"researchers":[119],"engage":[121],"innovate":[123],"field.":[126]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}