S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation models, where LLM-as-Aligner methods align graph and text representations through the semantic knowledge of large language models. However, these methods usually assume that node texts provide sufficient and reliable supervision, an assumption often violated in real-world sparse TAGs. When textual anchors are missing, noisy, or uneven across domains, graph structures must be aligned with weak semantic evidence, leading to unreliable structure-semantics correspondence and sparsity-induced transfer bias. This paper presents S2Aligner, a sparsity-aware and structure-enhanced LLM-as-Aligner fram...
| Authors | Yuhan Wang, Haopeng Zhang, Yibo Ding et al. |
|---|---|
| Primary category | cs.LG |
| Published | 18 May 2026 |
| Last updated at source | 20 May 2026 |
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