将语言模型与图神经网络融合，提升异质图节点分类效果
GMLM: Bridging Graph Neural Networks and Language Models for Heterophilic Node Classification
- 设计双向融合机制，对齐文本与图结构表示
- 在四个数据集上超越现有方法，德州数据集准确率提升超8%
- 适合做图文联合建模、异质图学习的研究者参考
将预训练语言模型（PLMs）与图神经网络（GNNs）结合，仍是文本丰富的异质图学习中的核心挑战。本文提出一种新型集成框架，实现强大预训练文本编码器与关系图卷积网络（R-GCNs）的有效融合。通过双向融合机制和对比性节点级优化，增强文本与结构表示的对齐。我们采用两种PLM：Snowflake-Embed（当前最优）和GTE-base，分别搭配R-GCN主干网络进行训练。在五个异质图基准测试上，该方法在四个数据集上达到领先性能，显著优于现有GNN及大语言模型方法。特别地，Snowflake-Embed + R-GCN在Texas数据集上准确率提升超过8%，在Wisconsin数据集上提升近5%。结果表明，该融合策略能有效推动文本丰富图表示学习的发展。
原文摘要 · Abstract (English)
Integrating Pre-trained Language Models (PLMs) with Graph Neural Networks (GNNs) remains a central challenge in text-rich heterophilic graph learning. We propose a novel integration framework that enables effective fusion between powerful pre-trained text encoders and Relational Graph Convolutional Networks (R-GCNs). Our method enhances the alignment of textual and structural representations through a bidirectional fusion mechanism and contrastive node-level optimization. To evaluate the approach, we train two variants using different PLMs: Snowflake-Embed (state-of-the-art) and GTE-base, each paired with an R-GCN backbone. Experiments on five heterophilic benchmarks demonstrate that our integration method achieves state-of-the-art results on four datasets, surpassing existing GNN and large language model-based approaches. Notably, Snowflake-Embed + R-GCN improves accuracy on the Texas dataset by over 8\% and on Wisconsin by nearly 5\%. These results highlight the effectiveness of our fusion strategy for advancing text-rich graph representation learning.
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