  Adversarial attacks can affect the performance of existing deep learning
models. With the increased interest in graph based machine learning techniques,
there have been investigations which suggest that these models are also
vulnerable to attacks. In particular, corruptions of the graph topology can
degrade the performance of graph based learning algorithms severely. This is
due to the fact that the prediction capability of these algorithms relies
mostly on the similarity structure imposed by the graph connectivity.
Therefore, detecting the location of the corruption and correcting the induced
errors becomes crucial. There has been some recent work which tackles the
detection problem, however these methods do not address the effect of the
attack on the downstream learning task. In this work, we propose an algorithm
that uses node copying to mitigate the degradation in classification that is
caused by adversarial attacks. The proposed methodology is applied only after
the model for the downstream task is trained and the added computation cost
scales well for large graphs. Experimental results show the effectiveness of
our approach for several real world datasets.
