{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/backdoor-attacks-to-graph-neural-networks","title":"Backdoor Attacks to Graph Neural Networks","arxiv_id":"2006.11165","date":"2020-06-19","proceeding":null,"authors":["Zaixi Zhang","Jinyuan Jia","Binghui Wang","Neil Zhenqiang Gong"],"abstract":"In this work, we propose the first backdoor attack to graph neural networks (GNN). Specifically, we propose a \\emph{subgraph based backdoor attack} to GNN for graph classification. 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Our empirical results show that the defense is effective in some cases but ineffective in other cases, highlighting the needs of new defenses for our backdoor attacks.","url_abs":"https://arxiv.org/abs/2006.11165v4","url_pdf":"https://arxiv.org/pdf/2006.11165v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"backdoor-attacks-to-graph-neural-networks","repo_url":"https://github.com/zaixizhang/graphbackdoor","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"backdoor-attacks-to-graph-neural-networks","repo_url":"https://github.com/ventr1c/ugba","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"backdoor-attack","task_name":"Backdoor Attack"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[{"method_slug":"randomized-smoothing","method_name":"Randomized Smoothing"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.11165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11165"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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