Web: http://arxiv.org/abs/2209.08064

Sept. 19, 2022, 1:12 a.m. | Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie

cs.LG updates on arXiv.org arxiv.org

Node embedding methods map network nodes to low dimensional vectors that can
be subsequently used in a variety of downstream prediction tasks. The
popularity of these methods has significantly increased in recent years, yet,
their robustness to perturbations of the input data is still poorly understood.
In this paper, we assess the empirical robustness of node embedding models to
random and adversarial poisoning attacks. Our systematic evaluation covers
representative embedding methods based on Skip-Gram, matrix factorization, and
deep neural networks. …

arxiv embedding evaluation node robustness

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