{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:03:08Z","timestamp":1775228588785,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["Emmy Noether grant GU 1409\/2-1"],"award-info":[{"award-number":["Emmy Noether grant GU 1409\/2-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330905","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"246-256","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":87,"title":["Certifiable Robustness and Robust Training for Graph Convolutional Networks"],"prefix":"10.1145","author":[{"given":"Daniel","family":"Z\u00fcgner","sequence":"first","affiliation":[{"name":"Technical University of Munich, Garching, Germany"}]},{"given":"Stephan","family":"G\u00fcnnemann","sequence":"additional","affiliation":[{"name":"Technical University of Munich, Garching, Germany"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Aleksandar Bojchevski and Stephan G\u00fc nnemann. 2019. Adversarial Attacks on Node Embeddings via Graph Poisoning. In ICML.  Aleksandar Bojchevski and Stephan G\u00fc nnemann. 2019. Adversarial Attacks on Node Embeddings via Graph Poisoning. In ICML."},{"key":"e_1_3_2_1_2_1","series-title":"Adaptive Computation and Machine Learning series","volume-title":"Semi-Supervised Learning","author":"Chapelle Olivier"},{"key":"e_1_3_2_1_3_1","unstructured":"Francesco Croce Maksym Andriushchenko and Matthias Hein. 2018. Provable robustness of relu networks via maximization of linear regions. In AISTATS .  Francesco Croce Maksym Andriushchenko and Matthias Hein. 2018. Provable robustness of relu networks via maximization of linear regions. In AISTATS ."},{"key":"e_1_3_2_1_4_1","unstructured":"Hanjun Dai Hui Li Tian Tian Xin Huang Lin Wang Jun Zhu and Le Song. 2018. Adversarial Attack on Graph Structured Data. In ICML .  Hanjun Dai Hui Li Tian Tian Xin Huang Lin Wang Jun Zhu and Le Song. 2018. Adversarial Attack on Graph Structured Data. In ICML ."},{"key":"e_1_3_2_1_5_1","volume-title":"mbox","author":"Defferrard Micha\u00eb","year":"2016"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3055540.3055554"},{"key":"e_1_3_2_1_7_1","volume-title":"Dahl","author":"Gilmer Justin","year":"2017"},{"key":"e_1_3_2_1_8_1","unstructured":"Ian J Goodfellow Jonathon Shlens and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. In ICLR .  Ian J Goodfellow Jonathon Shlens and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. In ICLR ."},{"key":"e_1_3_2_1_9_1","unstructured":"William L Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS .   William L Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS ."},{"key":"e_1_3_2_1_10_1","unstructured":"Matthias Hein and Maksym Andriushchenko. 2017. Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. In NIPS . 2263--2273.   Matthias Hein and Maksym Andriushchenko. 2017. Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. In NIPS . 2263--2273."},{"key":"e_1_3_2_1_11_1","volume-title":"BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. In SDM. 495--503.","author":"Hooi Bryan","year":"2016"},{"key":"e_1_3_2_1_12_1","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR .  Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR ."},{"key":"e_1_3_2_1_13_1","unstructured":"Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In ICLR .  Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In ICLR ."},{"key":"e_1_3_2_1_14_1","unstructured":"Balaji Lakshminarayanan Alexander Pritzel and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. In NIPS .   Balaji Lakshminarayanan Alexander Pritzel and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. In NIPS ."},{"key":"e_1_3_2_1_15_1","volume-title":"Data Classification: Algorithms and Applications","volume":"399","author":"London Ben","year":"2014"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"e_1_3_2_1_17_1","volume-title":"IEEE Symposium on Security and Privacy .","author":"Nicolas"},{"key":"e_1_3_2_1_18_1","unstructured":"Aditi Raghunathan Jacob Steinhardt and Percy S Liang. 2018. Semidefinite relaxations for certifying robustness to adversarial examples. In NIPS .   Aditi Raghunathan Jacob Steinhardt and Percy S Liang. 2018. Semidefinite relaxations for certifying robustness to adversarial examples. In NIPS ."},{"key":"e_1_3_2_1_19_1","volume-title":"Collective classification in network data. AI magazine","author":"Sen Prithviraj","year":"2008"},{"key":"e_1_3_2_1_20_1","unstructured":"Eric Wong and Zico Kolter. 2018. Provable defenses against adversarial examples via the convex outer adversarial polytope. In ICML . 5283--5292.  Eric Wong and Zico Kolter. 2018. Provable defenses against adversarial examples via the convex outer adversarial polytope. In ICML . 5283--5292."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial Attacks on Graph Neural Networks via Meta Learning. In ICLR .  Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial Attacks on Graph Neural Networks via Meta Learning. In ICLR .","DOI":"10.24963\/ijcai.2019\/872"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330905","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330905","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:03Z","timestamp":1750206363000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":22,"alternative-id":["10.1145\/3292500.3330905","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330905","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}