  Deep learning has achieved remarkable success in graph-related tasks, yet
this accomplishment heavily relies on large-scale high-quality annotated
datasets. However, acquiring such datasets can be cost-prohibitive, leading to
the practical use of labels obtained from economically efficient sources such
as web searches and user tags. Unfortunately, these labels often come with
noise, compromising the generalization performance of deep networks. To tackle
this challenge and enhance the robustness of deep learning models against label
noise in graph-based tasks, we propose a method called ERASE (Error-Resilient
representation learning on graphs for lAbel noiSe tolerancE). The core idea of
ERASE is to learn representations with error tolerance by maximizing coding
rate reduction. Particularly, we introduce a decoupled label propagation method
for learning representations. Before training, noisy labels are pre-corrected
through structural denoising. During training, ERASE combines prototype
pseudo-labels with propagated denoised labels and updates representations with
error resilience, which significantly improves the generalization performance
in node classification. The proposed method allows us to more effectively
withstand errors caused by mislabeled nodes, thereby strengthening the
robustness of deep networks in handling noisy graph data. Extensive
experimental results show that our method can outperform multiple baselines
with clear margins in broad noise levels and enjoy great scalability. Codes are
released at https://github.com/eraseai/erase.
