TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
Text-attributed graphs (TAGs), where each node carries a natural language description, require models to jointly reason over text and graph topology. Existing approaches often handle the two modalities separately: graph neural networks operate on shallow text features, while hybrids of LLMs and graphs use the language model mainly as a text encoder and delegate structure learning to a separate graph module. We propose method that unifies textual reasoning and graph message passing within a masked diffusion language model, a language model with bidirectional attention and generative decoding. For each graph instance, method linearises a sampled local neighbourhood into a token sequence and...
| Authors | Lingjie Chen, Yuanchen Bei, Haobo Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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