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. |
Similar Papers
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. |
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs (2020.tacl-1)
Copied to clipboard
| Challenge: | Recent graph-to-text models generate text from graph data using global or local aggregation . global node encoding allows explicit communication between two distant nodes, but fails to capture long-range relationships. |
| Approach: | They propose to combine global and local aggregation to learn node representations . they propose to use global and locally encoding to learn contextualized node embeddings based on graph data . |
| Outcome: | The proposed models outperform state-of-the-art models on two graph-to-text datasets by 18.01 and 63.69 points. |
CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing structure-aware approaches treat structure as serialized text prompts or auxiliary training objectives, failing to provide explicit guidance during inference. |
| Approach: | They propose a plug-and-play method that enhances Large Language Models with Code Graph information through an external, trainable Bridge module. |
| Outcome: | The proposed method decouples structural reasoning from textual generation without updating the backbone. |
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. |
GRAFF: GRaph-Augmented Fine-grained Fusion for Large Language Models (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to integrate graphs into LLMs compress the graph's structural information into a single token, restricting their ability to capture deep semantic and structural information. |
| Approach: | They propose a method that integrates fine-grained node-level structural information with corresponding text entities to LLMs via a lightweight, structure adapter module. |
| Outcome: | The proposed method outperforms baseline models in graph-based question answering by 10.24%. |
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. |
Beyond the Scientific Document: A Citation-Aware Multi-Granular Summarization Approach with Heterogeneous Graphs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results demonstrate that our model outperforms existing approaches for summarizing documents. |
| Approach: | proposed model constructs a heterogeneous graph to represent a document and its relevant external citations. |
| Outcome: | The proposed model outperforms existing models in three different scenarios. |
Graph Refinement for Coreference Resolution (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing models for coreference resolution are based on independent mention pair-wise decisions. |
| Approach: | They propose a model that learns coreference at the document-level and takes global decisions. |
| Outcome: | The proposed model improves over baselines, reinforcing the hypothesis that document-level information improves conference resolution. |
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to document-level relation extraction are difficult to establish direct connections between distant entity pairs. |
| Approach: | They propose a global context-enhanced Graph Convolutional Network model which captures rich global context information of entities in a document. |
| Outcome: | The proposed model captures rich global context information of entities in a document. |
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. |