  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.
