{"id":"https://openalex.org/W3007657110","doi":"https://doi.org/10.1109/bigdata47090.2019.9006004","title":"Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks","display_name":"Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007657110","doi":"https://doi.org/10.1109/bigdata47090.2019.9006004","mag":"3007657110"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006004","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2002.08012","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027492788","display_name":"Tsubasa Takahashi","orcid":"https://orcid.org/0000-0002-0646-0222"},"institutions":[{"id":"https://openalex.org/I4210096607","display_name":"Line Corporation (Japan)","ror":"https://ror.org/00qg8pm87","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210096607","https://openalex.org/I60922564"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tsubasa Takahashi","raw_affiliation_strings":["LINE Corporation"],"affiliations":[{"raw_affiliation_string":"LINE Corporation","institution_ids":["https://openalex.org/I4210096607"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5027492788"],"corresponding_institution_ids":["https://openalex.org/I4210096607"],"apc_list":null,"apc_paid":null,"fwci":2.8932,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.9311386,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1395","last_page":"1400"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9836999773979187,"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":0.9398000240325928,"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":0.749373733997345},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7483948469161987},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.647327184677124},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5510748028755188},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5213904976844788},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4222659170627594},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4037158489227295},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39336955547332764}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.749373733997345},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7483948469161987},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.647327184677124},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5510748028755188},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5213904976844788},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4222659170627594},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4037158489227295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39336955547332764},{"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006004","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2002.08012","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.08012","pdf_url":"https://arxiv.org/pdf/2002.08012","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2002.08012","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.08012","pdf_url":"https://arxiv.org/pdf/2002.08012","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W2078483536","https://openalex.org/W2153959628","https://openalex.org/W2154851992","https://openalex.org/W2162630660","https://openalex.org/W2460937040","https://openalex.org/W2535873859","https://openalex.org/W2604906612","https://openalex.org/W2624431344","https://openalex.org/W2626473047","https://openalex.org/W2759471388","https://openalex.org/W2766462876","https://openalex.org/W2771035597","https://openalex.org/W2785755647","https://openalex.org/W2787337315","https://openalex.org/W2787708942","https://openalex.org/W2798302089","https://openalex.org/W2803831897","https://openalex.org/W2803850896","https://openalex.org/W2807021761","https://openalex.org/W2871311606","https://openalex.org/W2898435086","https://openalex.org/W2913949296","https://openalex.org/W2914953695","https://openalex.org/W2949208225","https://openalex.org/W2951086007","https://openalex.org/W2962756421","https://openalex.org/W2962767366","https://openalex.org/W2963143631","https://openalex.org/W2963207607","https://openalex.org/W2963464736","https://openalex.org/W2963496101","https://openalex.org/W2963556353","https://openalex.org/W2963857521","https://openalex.org/W2964015378","https://openalex.org/W2964082701","https://openalex.org/W2964153729","https://openalex.org/W2964253222","https://openalex.org/W2964282455","https://openalex.org/W2964283260","https://openalex.org/W2966149470","https://openalex.org/W2999612054","https://openalex.org/W3007657110","https://openalex.org/W3098276446","https://openalex.org/W3100848837","https://openalex.org/W3104097132","https://openalex.org/W4293846201","https://openalex.org/W4294558607","https://openalex.org/W4297571622","https://openalex.org/W4299959846","https://openalex.org/W4300511536","https://openalex.org/W6631190155","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6748320467","https://openalex.org/W6748475379"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119","https://openalex.org/W4285226279","https://openalex.org/W4288019534"],"abstract_inverted_index":{"Graph":[0],"convolutional":[1,25,163,194],"neural":[2,164,195],"networks,":[3],"which":[4,74,125],"learn":[5],"aggregations":[6],"over":[7],"neighbor":[8],"nodes,":[9],"have":[10,166],"achieved":[11],"great":[12],"performance":[13],"in":[14,154,187],"node":[15,26,56,95,107,133],"classification":[16,41],"tasks.":[17],"However,":[18],"recent":[19],"studies":[20],"reported":[21],"that":[22,93,160],"such":[23,64],"graph":[24,37,53,162,193],"classifier":[27,96],"can":[28,43,59,97,181],"be":[29,44,98,169,182],"deceived":[30,99,170],"by":[31,46,102,171],"adversarial":[32,66,72],"perturbations":[33,73,128],"on":[34,77,129],"graphs.":[35],"Abusing":[36],"convolutions,":[38],"a":[39,55,105,122,131,185],"node's":[40],"result":[42],"influenced":[45],"poisoning":[47,103],"its":[48],"neighbors.":[49,177],"Given":[50],"an":[51],"attributed":[52],"and":[54],"classifier,":[57],"how":[58],"we":[60,69,91,120],"evaluate":[61],"robustness":[62],"against":[63],"indirect":[65,173],"attacks?":[67],"Can":[68],"generate":[70],"strong":[71],"are":[75],"effective":[76],"not":[78],"only":[79],"one-hop":[80],"neighbors,":[81],"but":[82],"more":[83,111],"far":[84,112,134],"from":[85,113,135,151],"the":[86,94,114,118,136,152],"target?":[87],"In":[88,138],"this":[89],"paper,":[90],"demonstrate":[92,159],"with":[100,197],"high-confidence":[101],"just":[104,130],"single":[106,132],"even":[108],"two-hops":[109,150],"or":[110],"target.":[115,137],"Towards":[116],"achieving":[117],"attack,":[119],"propose":[121],"new":[123],"approach":[124],"searches":[126],"smaller":[127],"our":[139,141,172],"experiments,":[140],"proposed":[142,179],"method":[143],"shows":[144],"99%":[145],"attack":[146,174,180],"success":[147],"rate":[148],"within":[149,175],"target":[153],"two":[155],"datasets.":[156],"We":[157],"also":[158],"m-layer":[161],"networks":[165,196],"chance":[167],"to":[168,191],"m-hop":[176],"The":[178],"used":[183],"as":[184],"benchmark":[186],"future":[188],"defense":[189],"attempts":[190],"develop":[192],"having":[198],"adversary":[199],"robustness.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}