mlsanityData to 7 Oct 2026

Bridging Local Details and Global Context in Text-Attributed Graphs

arXiv 2406.12608 ↗First posted 18 Jun 2024cs.CL cs.AIcited by 15

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Abstract (arXiv)

Representation learning on text-attributed graphs (TAGs) is vital for real-world applications, as they combine semantic textual and contextual structural information. Research in this field generally consist of two main perspectives: local-level encoding and global-level aggregating, respectively refer to textual node information unification (e.g., using Language Models) and structure-augmented modeling (e.g., using Graph Neural Networks). Most existing works focus on combining different information levels but overlook the interconnections, i.e., the contextual textual information among nodes, which provides semantic insights to bridge local and global levels. In this paper, we propose GraphBridge, a multi-granularity integration framework that bridges local and global perspectives by leveraging contextual textual information, enhancing fine-grained understanding of TAGs. Besides, to tackle scalability and efficiency challenges, we introduce a graphaware token reduction module. Extensive experiments across various models and datasets show that our method achieves state-of-theart performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

23 of 38 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
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  7. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  8. 2017Graph Attention Networkscited by 8,854
  9. 2017Inductive Representation Learning on Large Graphscited by 6,284
  10. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,581
  11. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  12. 2020Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networkscited by 253
11 more
  1. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
  2. 2022Learning on Large-scale Text-attributed Graphs via Variational Inferencecited by 153
  3. 2021GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graphcited by 143
  4. 2021Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Predictioncited by 137
  5. 2023SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learningcited by 73
  6. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  7. 2022BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotationcited by 54
  8. 2024Efficient Tuning and Inference for Large Language Models on Textual Graphscited by 54
  9. 2021TR-BERT: Dynamic Token Reduction for Accelerating BERT Inferencecited by 40
  10. 2023GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learningcited by 32
  11. 2023Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfercited by 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 5.

15 papers.

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  1. 2024Graph Retrieval-Augmented Generation: A Surveycited by 320
  2. 2024GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphscited by 58
  3. 2025SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Paperscited by 42
  4. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  5. 2024SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generationcited by 6
  6. 2025Toward General and Robust LLM-enhanced Text-attributed Graph Learningcited by 6
  7. 2024Graph Triple Attention Network: A Decoupled Perspectivecited by 2
  8. 2025HiTeC: Hierarchical Contrastive Learning on Text-Attributed Hypergraph with Semantic-Aware Augmentationcited by 2
  9. 2025Towards Efficient LLM-aware Heterogeneous Graph Learningcited by 2
  10. 2025Optimize Incompatible Parameters through Compatibility-aware Knowledge Integrationcited by 0
  11. 2026DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphscited by 0
  12. 2026TaLK: Text-attributed Graph Dataset Distillation via Coupling Language Model with Graph-Aware Kernelcited by 0
3 more
  1. 2026Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detectioncited by 0
  2. 2026TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learningcited by 0
  3. 2026Trojaning the Alignment: Stealthy Backdoor Attacks against Graph Foundation Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process
  2. 2025SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs
  3. 2024Distilling Large Language Models for Text-Attributed Graph Learning
  4. 2025Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment
  5. 2025Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications
  6. 2025When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected
  7. 2026TemporalGraphLLM: Temporal Graph Neural Networks with Large Language Models for Dynamic Text-Attributed Graphs
  8. 2026Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compression