GMLM: Bridging Graph Neural Networks and Language Models for Heterophilic Node Classification

Abstract: Integrating structured graph data with rich textual information from nodes poses a significant challenge, particularly for heterophilic node classification. Current approaches often struggle with computational costs or effective fusion of disparate modalities. We propose \textbf{Graph Masked LLM (GMLM)}, a novel architecture efficiently combining Graph Neural Networks (GNNs) with Pre-trained LLMs (PLMs). GMLM introduces three key innovations: (i) a \textbf{dynamic active node selection} strategy for scalable PLM text processing; (ii) a GNN-specific \textbf{contrastive pretraining stage} using soft masking with a learnable graph \texttt{[MASK]} token for robust structural representations; and (iii) a \textbf{dedicated fusion module} integrating RGCN-based GNN embeddings with PLM (GTE-Small & DistilBERT) embeddings. Extensive experiments on heterophilic benchmarks (Cornell, Wisconsin, Texas) demonstrate GMLM's superiority. Notably, GMLM(DistilBERT) achieves significant performance gains, improving accuracy by over \textbf{4.7\%} on Cornell and over \textbf{2.0\%} on Texas compared to the previous best-performing baselines. This work underscores the benefits of targeted PLM engagement and modality-specific pretraining for improved, efficient learning on text-rich graphs.