Text-attributed graphs (TAGs) are prevalent on the web and research over TAGs such as citation networks, e-commerce networks and social networks has attracted considerable attention in the web community. Recently, large language models (LLMs) have demonstrated exceptional capabilities across a wide range of tasks. However, the existing works focus on harnessing the potential of LLMs solely relying on prompts to convey graph structure information to LLMs, thus suffering from insufficient understanding of the complex structural relationships within TAGs. To address this problem, in this paper we present the Disentangled Graph-Text Learner (DGTL) model, which is able to enhance the reasoning and predicting capabilities of LLMs for TAGs. Our proposed DGTL model incorporates graph structure information through tailored disentangled graph neural network (GNN) layers, enabling LLMs to capture the intricate relationships hidden in text-attributed graphs from multiple structural factors. Furthermore, DGTL operates with frozen pre-trained LLMs, reducing computational costs and allowing much more flexibility in combining with different LLM models. Experimental evaluations demonstrate the effectiveness of the proposed DGTL model on achieving superior or comparable performance over state-of-the-art baselines. Additionally, we also demonstrate that our DGTL model can offer natural language explanations for predictions, thereby significantly enhancing model interpretability.

翻译：文本属性图（TAGs）在网络中普遍存在，对引文网络、电子商务网络和社交网络等TAG的研究已引起网络社区的广泛关注。近期，大语言模型（LLMs）在各类任务中展现出卓越能力。然而，现有工作仅依赖提示词向LLMs传递图结构信息，导致对TAGs中复杂结构关系的理解不足。为解决这一问题，本文提出解耦图-文本学习器（DGTL）模型，该模型能够增强LLMs对TAGs的推理与预测能力。所提出的DGTL模型通过定制的解耦图神经网络（GNN）层融入图结构信息，使LLMs能够从多个结构因素中捕捉文本属性图中隐藏的复杂关系。此外，DGTL采用冻结的预训练LLMs，既降低了计算成本，又增强了与不同LLM模型结合的灵活性。实验评估表明，所提出的DGTL模型在性能上优于或可比肩现有最优基线。同时，我们还证明DGTL模型能为预测结果提供自然语言解释，从而显著提升模型的可解释性。