  Graph neural networks based on message-passing mechanisms have achieved
advanced results in graph classification tasks. However, their generalization
performance degrades when noisy labels are present in the training data. Most
existing noisy labeling approaches focus on the visual domain or graph node
classification tasks and analyze the impact of noisy labels only from a utility
perspective. Unlike existing work, in this paper, we measure the effects of
noise labels on graph classification from data privacy and model utility
perspectives. We find that noise labels degrade the model's generalization
performance and enhance the ability of membership inference attacks on graph
data privacy. To this end, we propose the robust graph neural network approach
with noisy labeled graph classification. Specifically, we first accurately
filter the noisy samples by high-confidence samples and the first feature
principal component vector of each class. Then, the robust principal component
vectors and the model output under data augmentation are utilized to achieve
noise label correction guided by dual spatial information. Finally, supervised
graph contrastive learning is introduced to enhance the embedding quality of
the model and protect the privacy of the training graph data. The utility and
privacy of the proposed method are validated by comparing twelve different
methods on eight real graph classification datasets. Compared with the
state-of-the-art methods, the RGLC method achieves at most and at least 7.8%
and 0.8% performance gain at 30% noisy labeling rate, respectively, and reduces
the accuracy of privacy attacks to below 60%.
