Learning Noise-Resilient and Transferable Graph-Text Alignment via Dynamic Quality Assessment

• URL: http://arxiv.org/abs/2510.19384v1
• Date: Wed, 22 Oct 2025 09:01:17 GMT
• Title: Learning Noise-Resilient and Transferable Graph-Text Alignment via Dynamic Quality Assessment
• Authors: Yuhang Liu, Minglai Shao, Zengyi Wo, Yunlong Chu, Bing Hao, Shengzhong Liu, Ruijie Wang, Jianxin Li,
• Abstract summary: Pre-training Graph Foundation Models (GFMs) on text-attributed graphs (TAGs) is central to web-scale applications such as search, recommendation, and knowledge discovery.<n> existing CLIP-style graph-text amplifies face two key limitations: they assume strict one-to-one correspondences between nodes and texts, and they rely on static alignment objectives that cannot adapt to varying data quality, making them brittle under noisy supervision.<n>We propose ADAligner, a quality-aware graphtext alignment framework that dynamically adjusts between expressive many-to-many and conservative one-to-one objectives according to supervision quality
• Score: 19.204800655283744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-training Graph Foundation Models (GFMs) on text-attributed graphs (TAGs) is central to web-scale applications such as search, recommendation, and knowledge discovery. However, existing CLIP-style graph-text aligners face two key limitations: they assume strict one-to-one correspondences between nodes and texts, overlooking the inherent many-to-many relations in real-world graphs; and they rely on static alignment objectives that cannot adapt to varying data quality, making them brittle under noisy supervision. Together, these limitations expose a core dilemma: embracing expressive many-to-many alignment amplifies noise, while reverting to strict one-to-one strategies sacrifices semantic diversity and fails to handle inherently mismatched pairs. To address these challenges, we propose ADAligner, a dynamic, quality-aware graph-text alignment framework that dynamically adjusts between expressive many-to-many and conservative one-to-one objectives according to supervision quality. ADAligner estimates batch-level alignment reliability in real time and adapts its optimization accordingly, promoting soft, subgraph-level many-to-many alignment when supervision is clean, while emphasizing reliable one-to-one alignment by dynamically filtering low-confidence pairs under noise. Theoretically, we prove that this dynamic mechanism forms a stable negative feedback process, ensuring convergence and robustness. Comprehensive experiments on nine diverse TAG datasets demonstrate that ADAligner consistently outperforms prior graph-text aligners on zero-/few-shot node classification, link prediction and cross-modal retrieval tasks. It maintains strong robustness under noisy supervision and accelerates pre-training by approximately 2 to 3 times compared to multimodal baselines, establishing a scalable and reliable foundation for graph-text representation learning in real-world web environments.

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