Differentiated Information Mining: Semi-Supervised Graph Learning with Independent Patterns

Graph pseudo-labeling is an effective semi-supervised learning (SSL) approach to improve graph neural networks (GNNs) by leveraging unlabeled data. However, its success heavily depends on the reliability of pseudo-labels, which can often result in confirmation bias and training instability. To address these challenges, we propose a dual-layer consistency semi-supervised framework (DiPat), which integrates an internal differentiating pattern consistency mechanism and an external multimodal knowledge verification mechanism. In the internal layer, DiPat extracts multiple differentiating patterns from a single information source and enforces their consistency to improve the reliability of intrinsic decisions. During the supervised training phase, the model learns to extract and separate these patterns. In the semi-supervised learning phase, the model progressively selects highly consistent samples and ranks pseudo-labels based on the minimum margin principle, mitigating the overconfidence problem common in confidence-based or ensemble-based methods. In the external layer, DiPat also integrates large multimodal language models (MLLMs) as auxiliary information sources. These models provide latent textual knowledge to cross-validate internal decisions and introduce a responsibility scoring mechanism to filter out inconsistent or unreliable external judgments. Extensive experiments on multiple benchmark datasets show that DiPat demonstrates superior robustness and generalization in low-label settings, consistently outperforming strong baseline methods.