  The adversarial attack methods based on gradient information can adequately
find the perturbations, that is, the combinations of rewired links, thereby
reducing the effectiveness of the deep learning model based graph embedding
algorithms, but it is also easy to fall into a local optimum. Therefore, this
paper proposes a Momentum Gradient Attack (MGA) against the GCN model, which
can achieve more aggressive attacks with fewer rewiring links. Compared with
directly updating the original network using gradient information, integrating
the momentum term into the iterative process can stabilize the updating
direction, which makes the model jump out of poor local optimum and enhance the
method with stronger transferability. Experiments on node classification and
community detection methods based on three well-known network embedding
algorithms show that MGA has a better attack effect and transferability.
