Graph Neural Network Based on Weak Information Modeling

This paper proposes a Weak Information Graph Neural Network (WIGNN) for weak information modeling. Firstly, WIGNN introduces a multi-scale feature propagation mechanism that iteratively aggregates both local and global information, thereby enhancing node representations. Secondly, the authors design a pseudo label optimization and semantic alignment module that leverages limited labeled data alongside generated pseudo labels to construct class prototypes, reinforcing semantic consistency of nodes across different views. Finally, WIGNN incorporates a supervised contrastive learning module that aggregates representations for the similar node in the original and augmented graphs while pushing apart representations of nodes from different classes, which effectively mitigates weak feature issues. Extensive experiments on several public benchmark datasets demonstrate that WIGNN significantly outperforms leading baselines under extreme conditions of weak structure, weak features, and weak labels, exhibiting superior generalization and robustness.