Graph Partial Label Learning with Potential Cause Discovering
Hang Gao Jiaguo Yuan Jiangmeng Li Chengyu Yao Fengge Wu Junsuo Zhao Changwen Zheng
Graph Neural Networks (GNNs) have gained considerable attention for their
potential in addressing challenges posed by complex graph-structured data in
diverse domains. However, accurately annotating graph data for training is
difficult due to the inherent complexity and interconnectedness of graphs. To
tackle this issue, we propose a novel graph representation learning method that
enables GNN models to effectively learn discriminative information even in the
presence of noisy labels within the context of Partially Labeled Learning
(PLL). PLL is a critical weakly supervised learning problem, where each
training instance is associated with a set of candidate labels, including both
the true label and additional noisy labels. Our approach leverages potential
cause extraction to obtain graph data that exhibit a higher likelihood of
possessing a causal relationship with the labels. By incorporating auxiliary
training based on the extracted graph data, our model can effectively filter
out the noise contained in the labels. We support the rationale behind our
approach with a series of theoretical analyses. Moreover, we conduct extensive
evaluations and ablation studies on multiple datasets, demonstrating the
superiority of our proposed method.