Summary of Uniglm: Training One Unified Language Model For Text-attributed Graph Embedding, by Yi Fang et al.
UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding
by Yi Fang, Dongzhe Fan, Sirui Ding, Ninghao Liu, Qiaoyu Tan
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel Unified Graph Language Model (UniGLM) framework for representation learning on text-attributed graphs (TAGs). UniGLM generalizes well to both in-domain and cross-domain TAGs by leveraging multiple TAGs for joint fine-tuning, aligning text and graph structure from different aspects. The framework includes an adaptive positive sample selection technique for identifying structurally similar nodes and a lazy contrastive module that accelerates training by minimizing repetitive encoding calculations. UniGLM outperforms leading embedding baselines in terms of generalization and transfer learning across 9 benchmark TAGs, demonstrating its efficacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn from graphs with text descriptions attached to each node. Currently, the best methods for this are tailored to specific graph scenarios and can’t be used on different types of graphs. The authors created a new model called UniGLM that works well on both similar and very different types of graphs. It does this by learning from multiple graphs at once and using some clever techniques to speed up the training process. This model is better than existing ones for many tasks, including recommending things to people. |
Keywords
» Artificial intelligence » Embedding » Fine tuning » Generalization » Language model » Representation learning » Transfer learning
