  While graph neural networks have achieved state-of-the-art performances in
many real-world tasks including graph classification and node classification,
recent works have demonstrated they are also extremely vulnerable to
adversarial attacks. Most previous works have focused on attacking node
classification networks under impractical white-box scenarios. In this work, we
will propose a non-targeted Hard Label Black Box Node Injection Attack on Graph
Neural Networks, which to the best of our knowledge, is the first of its kind.
Under this setting, more real world tasks can be studied because our attack
assumes no prior knowledge about (1): the model architecture of the GNN we are
attacking; (2): the model's gradients; (3): the output logits of the target GNN
model. Our attack is based on an existing edge perturbation attack, from which
we restrict the optimization process to formulate a node injection attack. In
the work, we will evaluate the performance of the attack using three datasets,
COIL-DEL, IMDB-BINARY, and NCI1.

Machine Learning;Cryptography and Security;Social and Information Networks