Verifying message-passing neural networks via topology-based bounds
  tightening
Christopher Hojny Shiqiang Zhang Juan S. Campos Ruth Misener
Since graph neural networks (GNNs) are often vulnerable to attack, we need to
know when we can trust them. We develop a computationally effective approach
towards providing robust certificates for message-passing neural networks
(MPNNs) using a Rectified Linear Unit (ReLU) activation function. Because our
work builds on mixed-integer optimization, it encodes a wide variety of
subproblems, for example it admits (i) both adding and removing edges, (ii)
both global and local budgets, and (iii) both topological perturbations and
feature modifications. Our key technology, topology-based bounds tightening,
uses graph structure to tighten bounds. We also experiment with aggressive
bounds tightening to dynamically change the optimization constraints by
tightening variable bounds. To demonstrate the effectiveness of these
strategies, we implement an extension to the open-source branch-and-cut solver
SCIP. We test on both node and graph classification problems and consider
topological attacks that both add and remove edges.