  Along with the advent of deep neural networks came various methods of
exploitation, such as fooling the classifier or contaminating its training
data. Another such attack is known as model extraction, where provided API
access to some black box neural network, the adversary extracts the underlying
model. This is done by querying the model in such a way that the underlying
neural network provides enough information to the adversary to be
reconstructed. While several works have achieved impressive results with neural
network extraction in the propositional domain, this problem has not yet been
considered over the relational domain, where data samples are no longer
considered to be independent and identically distributed (iid). Graph Neural
Networks (GNNs) are a popular deep learning framework to perform machine
learning tasks over relational data. In this work, we formalize an instance of
GNN extraction, present a solution with preliminary results, and discuss our
assumptions and future directions.
