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

• URL: http://arxiv.org/abs/2210.14709v1
• Date: Wed, 26 Oct 2022 13:40:57 GMT
• Title: Learning on Large-scale Text-attributed Graphs via Variational Inference
• Authors: Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang
• Abstract summary: This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. 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 framework.
• Score: 44.558681850874336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 large language models and training GNNs on big graphs. 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 to separately train the two modules but at the same time allows 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.

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