# An Effective Approach for Node Classification in Textual Graphs

arXiv: https://arxiv.org/abs/2508.05836 · HTML: https://mlsanity.com/paper/2508.05836/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)
- [Do Transformers Really Perform Bad for Graph Representation?](https://mlsanity.com/paper/2106.05234/) (2021)
- [GPT-GNN: Generative Pre-Training of Graph Neural Networks](https://mlsanity.com/paper/2006.15437/) (2020)

## Abstract (arXiv)

Textual Attribute Graphs (TAGs) are critical for modeling complex networks like citation networks, but effective node classification remains challenging due to difficulties in integrating rich semantics from text with structural graph information. Existing methods often struggle with capturing nuanced domain-specific terminology, modeling long-range dependencies, adapting to temporal evolution, and scaling to massive datasets. To address these issues, we propose a novel framework that integrates TAPE (Text-Attributed Graph Representation Enhancement) with Graphormer. Our approach leverages a large language model (LLM), specifically ChatGPT, within the TAPE framework to generate semantically rich explanations from paper content, which are then fused into enhanced node representations. These embeddings are combined with structural features using a novel integration layer with learned attention weights. Graphormer's path-aware position encoding and multi-head attention mechanisms are employed to effectively capture long-range dependencies across the citation network. We demonstrate the efficacy of our framework on the challenging ogbn-arxiv dataset, achieving state-of-the-art performance with a classification accuracy of 0.772, significantly surpassing the best GCN baseline of 0.713. Our method also yields strong results in precision (0.671), recall (0.577), and F1-score (0.610). We validate our approach through comprehensive ablation studies that quantify the contribution of each component, demonstrating the synergy between semantic and structural information. Our framework provides a scalable and robust solution for node classification in dynamic TAGs, offering a promising direction for future research in knowledge systems and scientific discovery.