  While the use of graph-structured data in various fields is becoming
increasingly popular, it also raises concerns about the potential unauthorized
exploitation of personal data for training commercial graph neural network
(GNN) models, which can compromise privacy. To address this issue, we propose a
novel method for generating unlearnable graph examples. By injecting delusive
but imperceptible noise into graphs using our Error-Minimizing Structural
Poisoning (EMinS) module, we are able to make the graphs unexploitable.
Notably, by modifying only $5\%$ at most of the potential edges in the graph
data, our method successfully decreases the accuracy from ${77.33\%}$ to
${42.47\%}$ on the COLLAB dataset.
