Learning on Large-scale Text-attributed Graphs via Variational Inference

TL;DR

This paper introduces GLEM, a variational EM-based method for efficiently learning on large-scale text-attributed graphs by alternately training language models and GNNs, improving performance while reducing computational costs.

Contribution

It proposes a novel variational EM framework that separates training of language models and GNNs for large graphs, enabling scalable and effective learning.

Findings

GLEM outperforms existing methods on multiple datasets.

The approach reduces training time significantly.

It effectively integrates text and graph structure information.

Abstract

This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both the text and graph structure information with large language models and graph neural networks (GNNs). However, the problem becomes very challenging when graphs are large due to the high computational complexity brought by training large language models and GNNs together. In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM. Instead of simultaneously training large language models and GNNs on big graphs, GLEM proposes to alternatively update the two modules in the E-step and M-step. Such a procedure allows training the two modules separately while simultaneously allowing the two modules to interact and mutually enhance each other. Extensive experiments on multiple data sets demonstrate the efficiency and effectiveness of the proposed approach.