Temporal Dynamics-Aware Adversarial Attacks on Discrete-Time Dynamic Graph Models

Author:

Sharma Kartik1ORCID,Trivedi Rakshit2ORCID,Sridhar Rohit1ORCID,Kumar Srijan1ORCID

Affiliation:

1. Georgia Institute of Technology, Atlanta, USA

2. Massachusetts Institute of Technology, Boston, USA

Funder

Defense Advanced Research Projects Agency

NSF (National Science Foundation)

Publisher

ACM

Reference60 articles.

1. Graph based anomaly detection and description: a survey

2. Aleksandar Bojchevski and Stephan Günnemann . 2019 . Adversarial attacks on node embeddings via graph poisoning . In International Conference on Machine Learning. PMLR, 695--704 . Aleksandar Bojchevski and Stephan Günnemann. 2019. Adversarial attacks on node embeddings via graph poisoning. In International Conference on Machine Learning. PMLR, 695--704.

3. Stephen Boyd , Stephen P Boyd , and Lieven Vandenberghe . 2004. Convex optimization . Cambridge university press . Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe. 2004. Convex optimization. Cambridge university press.

4. Sébastien Bubeck et al. 2015. Convex optimization: Algorithms and complexity . Foundations and Trends® in Machine Learning , Vol. 8 , 3--4 ( 2015 ), 231--357. Sébastien Bubeck et al. 2015. Convex optimization: Algorithms and complexity. Foundations and Trends® in Machine Learning, Vol. 8, 3--4 (2015), 231--357.

5. Horst Bunke , Peter J Dickinson , Miro Kraetzl , and Walter D Wallis . 2007. A graph-theoretic approach to enterprise network dynamics . Vol. 24 . Springer Science & Business Media . Horst Bunke, Peter J Dickinson, Miro Kraetzl, and Walter D Wallis. 2007. A graph-theoretic approach to enterprise network dynamics. Vol. 24. Springer Science & Business Media.

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