| Challenge: | Existing models focus on either the text attribute or the graph structure, neglecting the other aspect. |
| Approach: | They propose a model that combines the strengths of both text-learning and graph-learning models in parallel. |
| Outcome: | The proposed model outperforms existing models on diverse datasets. |
Similar Papers
Unleashing the Power of Language Models in Text-Attributed Graph (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on graph learning on text-attributed graphs have been limited by memory cost and underutilization of relationships between nodes and words. |
| Approach: | They propose a Node Representation Update Pre-training Architecture based on Co-modeling text and graph to learn representations of papers and words simultaneously. |
| Outcome: | The proposed model outperforms baselines on the ogbn-arxiv benchmark dataset. |
Pretraining Language Models with Text-Attributed Heterogeneous Graphs (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing pretraining tasks for Language Models (LMs) focus on learning the textual information of each entity and overlook the crucial aspect of capturing topological connections among entities in TAHGs. |
| Approach: | They propose a topology-aware pretraining task that explicitly considers the topological and heterogeneous information in TAHGs by optimizing an LM and an auxiliary heterogenous graph neural network. |
| Outcome: | The proposed framework explicitly considers the topological and heterogeneous information in TAHGs. |
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. |
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. |
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent work on aspect-level sentiment classification has shown that syntactic information is effective in capturing long-range syntaktic relations that are obscure from the surface form. |
| Approach: | They propose a graph ensemble technique that integrates syntactic structures with GNNs to better leverage syntaktic information in the face of parsing errors. |
| Outcome: | The proposed model outperforms models with single dependency tree and beats other models without adding model parameters. |
Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Graph Neural Networks have been used for text classification, but only in domains with limited data characteristics. |
| Approach: | They compare graph representation methods for text classification using different architectures and setups. |
| Outcome: | The proposed graph representation methods outperform other models in document comprehension tasks. |
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. |
Controlled Transformation of Text-Attributed Graphs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Graph generation is the process of generating new graphs with similar attributes to real world graphs. |
| Approach: | They propose a controllable multi-objective translation model for text-attributed graphs that can translate a given source graph to a target graph while satisfying multiple desired graph attributes at granular level. |
| Outcome: | The proposed model can translate a given source graph to a target graph while satisfying multiple desired graph attributes at granular level. |
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. |
Graph-Based Semi-Supervised Learning for Natural Language Understanding (D19-53)
Copied to clipboard
| Challenge: | Semi-supervised learning is an efficient method to augment training data from unlabeled data. |
| Approach: | They propose semi-supervised learning models and their inductive variants for NLU and use them to find similar utterances and construct a graph. |
| Outcome: | The proposed model improves the error rate of the model by 5% using publicly available NLU data and models. |