  Deep learning is effective in graph analysis. It is widely applied in many
related areas, such as link prediction, node classification, community
detection, and graph classification etc. Graph embedding, which learns
low-dimensional representations for vertices or edges in the graph, usually
employs deep models to derive the embedding vector. However, these models are
vulnerable. We envision that graph embedding methods based on deep models can
be easily attacked using adversarial examples. Thus, in this paper, we propose
Graphfool, a novel targeted label adversarial attack on graph embedding. It can
generate adversarial graph to attack graph embedding methods via classifying
boundary and gradient information in graph convolutional network (GCN).
Specifically, we perform the following steps: 1),We first estimate the
classification boundaries of different classes. 2), We calculate the minimal
perturbation matrix to misclassify the attacked vertex according to the target
classification boundary. 3), We modify the adjacency matrix according to the
maximal absolute value of the disturbance matrix. This process is implemented
iteratively. To the best of our knowledge, this is the first targeted label
attack technique. The experiments on real-world graph networks demonstrate that
Graphfool can derive better performance than state-of-art techniques. Compared
with the second best algorithm, Graphfool can achieve an average improvement of
11.44% in attack success rate.
