  Benefiting from the message passing mechanism, Graph Neural Networks (GNNs)
have been successful on flourish tasks over graph data. However, recent studies
have shown that attackers can catastrophically degrade the performance of GNNs
by maliciously modifying the graph structure. A straightforward solution to
remedy this issue is to model the edge weights by learning a metric function
between pairwise representations of two end nodes, which attempts to assign low
weights to adversarial edges. The existing methods use either raw features or
representations learned by supervised GNNs to model the edge weights. However,
both strategies are faced with some immediate problems: raw features cannot
represent various properties of nodes (e.g., structure information), and
representations learned by supervised GNN may suffer from the poor performance
of the classifier on the poisoned graph. We need representations that carry
both feature information and as mush correct structure information as possible
and are insensitive to structural perturbations. To this end, we propose an
unsupervised pipeline, named STABLE, to optimize the graph structure. Finally,
we input the well-refined graph into a downstream classifier. For this part, we
design an advanced GCN that significantly enhances the robustness of vanilla
GCN without increasing the time complexity. Extensive experiments on four
real-world graph benchmarks demonstrate that STABLE outperforms the
state-of-the-art methods and successfully defends against various attacks.
