Uncertainty-Aware Robust Learning on Noisy Graphs
Shuyi Chen Kaize Ding Shixiang Zhu
Graph neural networks have shown impressive capabilities in solving various
graph learning tasks, particularly excelling in node classification. However,
their effectiveness can be hindered by the challenges arising from the
widespread existence of noisy measurements associated with the topological or
nodal information present in real-world graphs. These inaccuracies in
observations can corrupt the crucial patterns within the graph data, ultimately
resulting in undesirable performance in practical applications. To address
these issues, this paper proposes a novel uncertainty-aware graph learning
framework motivated by distributionally robust optimization. Specifically, we
use a graph neural network-based encoder to embed the node features and find
the optimal node embeddings by minimizing the worst-case risk through a minimax
formulation. Such an uncertainty-aware learning process leads to improved node
representations and a more robust graph predictive model that effectively
mitigates the impact of uncertainty arising from data noise. Our experimental
result shows that the proposed framework achieves superior predictive
performance compared to the state-of-the-art baselines under various noisy
settings.