Inferring Properties of Graph Neural Networks
CoRR(2024)
摘要
We propose GNNInfer, the first automatic property inference technique for
GNNs. To tackle the challenge of varying input structures in GNNs, GNNInfer
first identifies a set of representative influential structures that contribute
significantly towards the prediction of a GNN. Using these structures, GNNInfer
converts each pair of an influential structure and the GNN to their equivalent
FNN and then leverages existing property inference techniques to effectively
capture properties of the GNN that are specific to the influential structures.
GNNINfer then generalizes the captured properties to any input graphs that
contain the influential structures. Finally, GNNInfer improves the correctness
of the inferred properties by building a model (either a decision tree or
linear regression) that estimates the deviation of GNN output from the inferred
properties given full input graphs. The learned model helps GNNInfer extend the
inferred properties with constraints to the input and output of the GNN,
obtaining stronger properties that hold on full input graphs.
Our experiments show that GNNInfer is effective in inferring likely
properties of popular real-world GNNs, and more importantly, these inferred
properties help effectively defend against GNNs' backdoor attacks. In
particular, out of the 13 ground truth properties, GNNInfer re-discovered 8
correct properties and discovered likely correct properties that approximate
the remaining 5 ground truth properties. Using properties inferred by GNNInfer
to defend against the state-of-the-art backdoor attack technique on GNNs,
namely UGBA, experiments show that GNNInfer's defense success rate is up to 30
times better than existing baselines.
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