CoACL: Coupled Augmentation for Contrastive Learning on Text-Attributed Graphs Under Semantic Supervision from Large Language Models
Year: 2026
Type: article
Abstract: Text-attributed graphs (TAGs) couple graph topology with node-level text, but real data often contain spurious edges, missing links, and text–structure mismatch that destabilize learning under scarce labels. We propose CoACL (Coupled Augmentation for Contrastive Learning), a framework that uses LLM semantic supervision to denoise structural and textual information and alleviate data sparsity. CoACL first prunes the candidate edge space using structural similarity and then queries an LLM to discard suspicious edges and confirm plausible links, yielding semantically consistent positive and negative pairs. We further introduce keyword-focused text augmentations and learn coupled representations by optimizing a joint text–graph contrastive objective guided by semantics. Experiment results on Cora, PubMed, and the Open Graph Benchmark Arxiv dataset (OGBN-Arxiv) show that CoACL consistently outperforms strong baselines and yields up to 7.1% absolute improvement in node classification accuracy, with the largest gains in low-label regimes. By constraining LLM evaluation to similarity-based candidates, CoACL targets neighborhood-level noise with controlled cost.
Source: Electronics
Institution: Yanshan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7129022114
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