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Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs (2407.15431v1)

Published 22 Jul 2024 in cs.SI, cs.AI, and cs.LG

Abstract: The text-attributed graph (TAG) is one kind of important real-world graph-structured data with each node associated with raw texts. For TAGs, traditional few-shot node classification methods directly conduct training on the pre-processed node features and do not consider the raw texts. The performance is highly dependent on the choice of the feature pre-processing method. In this paper, we propose P2TAG, a framework designed for few-shot node classification on TAGs with graph pre-training and prompting. P2TAG first pre-trains the LLM (LM) and graph neural network (GNN) on TAGs with self-supervised loss. To fully utilize the ability of LLMs, we adapt the masked LLMing objective for our framework. The pre-trained model is then used for the few-shot node classification with a mixed prompt method, which simultaneously considers both text and graph information. We conduct experiments on six real-world TAGs, including paper citation networks and product co-purchasing networks. Experimental results demonstrate that our proposed framework outperforms existing graph few-shot learning methods on these datasets with +18.98% ~ +35.98% improvements.

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Authors (9)
  1. Huanjing Zhao (2 papers)
  2. Beining Yang (6 papers)
  3. Yukuo Cen (19 papers)
  4. Junyu Ren (2 papers)
  5. Chenhui Zhang (16 papers)
  6. Yuxiao Dong (119 papers)
  7. Evgeny Kharlamov (34 papers)
  8. Shu Zhao (31 papers)
  9. Jie Tang (302 papers)
Citations (4)
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