Representation learning on text-attributed graphs (TAGs) is vital for
real-world applications, as they combine semantic textual and contextual
structural information. Research in this field generally consist of two main
perspectives: local-level encoding and global-level aggregating, respectively
refer to textual node information unification (e.g., using Language Models) and
structure-augmented modeling (e.g., using Graph Neural Networks). Most existing
works focus on combining different information levels but overlook the
interconnections, i.e., the contextual textual information among nodes, which
provides semantic insights to bridge local and global levels. In this paper, we
propose GraphBridge, a multi-granularity integration framework that bridges
local and global perspectives by leveraging contextual textual information,
enhancing fine-grained understanding of TAGs. Besides, to tackle scalability
and efficiency challenges, we introduce a graphaware token reduction module.
Extensive experiments across various models and datasets show that our method
achieves state-of-theart performance, while our graph-aware token reduction
module significantly enhances efficiency and solves scalability issues.

提出了 GraphBridge，一个多粒度整合框架，通过利用上下文文本信息来构建 TAGs 的局部和全局视角的桥梁，以增强对 TAGs 的细粒度理解，并引入了一个图感知的标记缩减模块来处理可扩展性和效率挑战。通过在各种模型和数据集上进行大量实验，表明我们的方法在性能上达到了最先进的水平，而我们的图感知标记缩减模块显著提高了效率并解决了可扩展性问题。