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

Résumé fourni par la source

Pre-training graph foundation models (GFMs) on text-attributed graphs (TAGs) is important for web-scale retrieval and recommendation, where graph entities are matched with textual descriptions. Existing CLIP-style graph-text aligners typically assume one-to-one correspondence: each node is pulled close only to its paired text, and all other pairs are treated as negatives. This overlooks the many-to-many relations common in real TAGs, where a node and its local neighborhood can be semantically related to multiple texts, and vice versa. Meanwhile, TAG supervision is often imperfect: noisy or weak node-text links introduce false-positive pairs, causing contrastive learning to align mismatched semantics. These limitations reveal a fundamental trade-off: leveraging expressive many-to-many signals increases semantic coverage but may propagate errors under noise, whereas strict one-to-one training is more conservative yet still suffers when mismatched pairs remain in the training set. Therefore, we propose ADAligner, a quality-aware graph–text alignment framework that adapts between expressive many-to-many and conservative one-to-one objectives based on estimated alignment reliability. ADAligner tracks batch-level reliability online and adjusts optimization accordingly—promoting soft, subgraph-level alignment when supervision is clean while emphasizing reliable one-to-one alignment by filtering low-confidence pairs under noise. We provide theoretical analysis showing that this closed-loop adaptation is stable and convergent. Experiments on nine TAG benchmarks show that, under 30% mismatched node-text supervision, ADAligner consistently improves cross-modal retrieval by 144.70% on average, zero-/few-shot node classification by 26.13%, and link prediction by 4.70% over the strongest multimodal baseline, demonstrating strong robustness to alignment noise across both unsupervised and transfer settings. Our code is available at https://github.com/karmaisacat-13/ADAligner.

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