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.

通过 Disentangled Graph-Text Learner (DGTL) 模型，结合图结构信息和大型语言模型，提升了对文本属性图的推理能力和预测能力，并且通过提供自然语言解释来显著提高模型的解释性。