BridgeGLM: Bridging Graph and Language Spaces for Domain Generalization

Graph Neural Networks (GNNs) are effective for processing graph data; however, their heavy reliance on label information limits their generalization across domains. At the same time, Large Language Models (LLMs) have made significant progress across diverse domains, sparking growing interest in their potential for processing and understanding graph data. Nevertheless, their effectiveness is constrained by challenges such as space misalignment and global topology blindness, which arise because LLMs are trained primarily on Euclidean data rather than non-Euclidean structures. To address these challenges, we propose BridgeGLM, a novel framework that bridges graph and language spaces to improve domain generalization. BridgeGLM integrates topology-aware graph representations to capture higher-order structural relationships and employs a semantic-aware tokenizer to generate enriched node representations. Furthermore, we introduce three contrastive learning strategies based on graph interactions to effectively align graph and language representations. During testing, task-specific instruction templates facilitate zero-shot node classification. Extensive experiments on six datasets, covering both academic and recommendation graphs, show that BridgeGLM consistently outperforms state-of-the-art baselines across in-dataset, intra-domain, and cross-domain settings. In cross-domain settings, BridgeGLM achieves a 2-7% improvement in key performance metrics compared to the existing state-of-the-art method.