Papers with TAGs
Can LLMs Convert Graphs to Text-Attributed Graphs? (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches to model graph-structured data are limited by the availability of text-attributed graph data. |
| Approach: | They propose a method to convert existing graphs into text-attributed graphs using large language models. |
| Outcome: | The proposed method outperforms existing approaches that manually design node features on text-free graphs. |
Let’s Ask GNN: Empowering Large Language Model for Graph In-Context Learning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data. |
| Approach: | They propose a novel approach that leverages In-Context Learning to integrate graph data and task-specific information into large language models (LLMs) they employ a Graph Neural Network-powered structure-enhanced retriever to select labeled nodes across graphs, incorporating complex graph structures and their supervision signals. |
| Outcome: | Experiments on three tasks and seven LLMs show that AskGNN performs better than existing methods. |
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design (2025.findings-naacl)
Copied to clipboard
| Challenge: | Text-Attributed Graphs (TAGs) are a powerful tool for understanding complex systems and relationships. |
| Approach: | They propose a benchmark to evaluate large language models for graph-structured data using prompts. |
| Outcome: | The proposed benchmark outperforms state-of-the-art graph LLMs and graph neural networks on graph learning tasks without training. |
Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to learning text-attributed graphs neglect interaction between textual and structural information. |
| Approach: | They propose a framework that integrates textual and structural information into TAG learning . they propose combining semantic aggregation and structural aggregations to improve learning a . |
| Outcome: | The proposed framework outperforms state-of-the-art learning methods while requiring less resources. |
Fair Text-Attributed Graph Representation Learning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Text-Attributed Graphs (TAGs) inherit issues from Graph Neural Networks such as fairness. |
| Approach: | They propose to evolve LM-as-encoder to LM as-fair-encoding process to explore fairness in TAGRL. |
| Outcome: | The proposed process can be integrated with fairness-enhancing strategies on the GNNs decoder side. |
Bridging Local Details and Global Context in Text-Attributed Graphs (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies focus on combining different information levels but overlook interconnections, i.e., contextual textual information among nodes. |
| Approach: | They propose a framework that bridges local and global perspectives by leveraging contextual textual information. |
| Outcome: | The proposed framework achieves state-of-the-art performance while reducing tokens significantly. |
Out-of-Distribution Detection via LLM-Guided Outlier Generation for Text-attributed Graph (2025.findings-acl)
Copied to clipboard
| Challenge: | Text-Attributed Graphs (TAGs) are widely used in the real world. |
| Approach: | They propose to use Large Language Models to generate OOD-nodes with high quality . they also use LLMs to integrate existing nodes with LLM-generated edges . |
| Outcome: | The proposed method performs well on samples outside the In-Distribution (ID) data, but it is difficult to obtain high-quality OOD samples in the real world. |
Compressing LLM Knowledge into Graph Representations for Text-attributed Graphs Learning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing GNN-LLM approaches use large language models at inference time for processing text attributes, resulting in costly deployment. |
| Approach: | They propose a framework that internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. |
| Outcome: | The proposed framework internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. |
From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context (2026.acl-long)
Copied to clipboard
| Challenge: | Existing explanation methods for graph neural networks struggle to generate interpretable, fine-grained rationales. |
| Approach: | They propose a lightweight framework that uses large language models to generate interpretable explanations for GNNs. |
| Outcome: | The proposed framework generates interpretable explanations for GNN predictions using large language models. |