Text-rich graphs, prevalent in data mining contexts like e-commerce and
academic graphs, consist of nodes with textual features linked by various
relations. Traditional graph machine learning models, such as Graph Neural
Networks (GNNs), excel in encoding the graph structural information, but have
limited capability in handling rich text on graph nodes. Large Language Models
(LLMs), noted for their superior text understanding abilities, offer a solution
for processing the text in graphs but face integration challenges due to their
limitation for encoding graph structures and their computational complexities
when dealing with extensive text in large neighborhoods of interconnected
nodes. This paper introduces ``Hierarchical Compression'' (HiCom), a novel
method to align the capabilities of LLMs with the structure of text-rich
graphs. HiCom processes text in a node's neighborhood in a structured manner by
organizing the extensive textual information into a more manageable hierarchy
and compressing node text step by step. Therefore, HiCom not only preserves the
contextual richness of the text but also addresses the computational challenges
of LLMs, which presents an advancement in integrating the text processing power
of LLMs with the structural complexities of text-rich graphs. Empirical results
show that HiCom can outperform both GNNs and LLM backbones for node
classification on e-commerce and citation graphs. HiCom is especially effective
for nodes from a dense region in a graph, where it achieves a 3.48% average
performance improvement on five datasets while being more efficient than LLM
backbones.

在处理文本丰富的图结构数据中，本研究介绍了一种被称为 “Hierarchical Compression” 的新方法，通过将文本信息组织成可管理的层次结构并逐步压缩节点文本，使得大型语言模型能够更好地与文本丰富的图结构进行整合，不仅能保留文本的上下文信息，还能解决计算复杂性问题，并在电子商务和引用图上实现了比传统方法（如图神经网络和大型语言模型）更好的节点分类性能。