  Graph Neural Networks have demonstrated great success in various fields of
multimedia. However, the distribution shift between the training and test data
challenges the effectiveness of GNNs. To mitigate this challenge, Test-Time
Training (TTT) has been proposed as a promising approach. Traditional TTT
methods require a demanding unsupervised training strategy to capture the
information from test to benefit the main task. Inspired by the great
annotation ability of Large Language Models (LLMs) on Text-Attributed Graphs
(TAGs), we propose to enhance the test-time training on graphs with LLMs as
annotators. In this paper, we design a novel Test-Time Training pipeline,
LLMTTT, which conducts the test-time adaptation under the annotations by LLMs
on a carefully-selected node set. Specifically, LLMTTT introduces a hybrid
active node selection strategy that considers not only node diversity and
representativeness, but also prediction signals from the pre-trained model.
Given annotations from LLMs, a two-stage training strategy is designed to
tailor the test-time model with the limited and noisy labels. A theoretical
analysis ensures the validity of our method and extensive experiments
demonstrate that the proposed LLMTTT can achieve a significant performance
improvement compared to existing Out-of-Distribution (OOD) generalization
methods.
