  Recent work has shown that graph neural networks (GNNs) are vulnerable to
adversarial attacks on graph data. Common attack approaches are typically
informed, i.e. they have access to information about node attributes such as
labels and feature vectors. In this work, we study adversarial attacks that are
uninformed, where an attacker only has access to the graph structure, but no
information about node attributes. Here the attacker aims to exploit structural
knowledge and assumptions, which GNN models make about graph data. In
particular, literature has shown that structural node centrality and similarity
have a strong influence on learning with GNNs. Therefore, we study the impact
of centrality and similarity on adversarial attacks on GNNs. We demonstrate
that attackers can exploit this information to decrease the performance of GNNs
by focusing on injecting links between nodes of low similarity and,
surprisingly, low centrality. We show that structure-based uninformed attacks
can approach the performance of informed attacks, while being computationally
more efficient. With our paper, we present a new attack strategy on GNNs that
we refer to as Structack. Structack can successfully manipulate the performance
of GNNs with very limited information while operating under tight computational
constraints. Our work contributes towards building more robust machine learning
approaches on graphs.
