Graph convolution network (GCN) have achieved state-of-the-art performance in
the task of node prediction in the graph structure. However, with the gradual
various of graph attack methods, there are lack of research on the robustness
of GCN. At this paper, we will design a robust GCN method for node prediction
tasks. Considering the graph structure contains two types of information: node
information and connection information, and attackers usually modify the
connection information to complete the interference with the prediction results
of the node, we first proposed a method to hide the connection information in
the generator, named Anonymized GCN (AN-GCN). By hiding the connection
information in the graph structure in the generator through adversarial
training, the accurate node prediction can be completed only by the node number
rather than its specific position in the graph. Specifically, we first
demonstrated the key to determine the embedding of a specific node: the row
corresponding to the node of the eigenmatrix of the Laplace matrix, by target
it as the output of the generator, we designed a method to hide the node number
in the noise. Take the corresponding noise as input, we will obtain the
connection structure of the node instead of directly obtaining. Then the
encoder and decoder are spliced both in discriminator, so that after
adversarial training, the generator and discriminator can cooperate to complete
the encoding and decoding of the graph, then complete the node prediction.
Finally, All node positions can generated by noise at the same time, that is to
say, the generator will hides all the connection information of the graph
structure. The evaluation shows that we only need to obtain the initial
features and node numbers of the nodes to complete the node prediction, and the
accuracy did not decrease, but increased by 0.0293.