SGL-PT: A Strong Graph Learner with Graph Prompt Tuning

• URL: http://arxiv.org/abs/2302.12449v2
• Date: Tue, 15 Aug 2023 08:11:16 GMT
• Title: SGL-PT: A Strong Graph Learner with Graph Prompt Tuning
• Authors: Yun Zhu and Jianhao Guo and Siliang Tang
• Abstract summary: We propose a novel framework named SGL-PT which follows the learning strategy Pre-train, Prompt, and Predict''. Specifically, we raise a strong and universal pre-training task coined as SGL that acquires the complementary merits of generative and contrastive self-supervised graph learning. And aiming for graph classification task, we unify pre-training and fine-tuning by designing a novel verbalizer-free prompting function, which reformulates the downstream task in a similar format as pretext task.
• Score: 36.650472660276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, much exertion has been paid to design graph self-supervised methods to obtain generalized pre-trained models, and adapt pre-trained models onto downstream tasks through fine-tuning. However, there exists an inherent gap between pretext and downstream graph tasks, which insufficiently exerts the ability of pre-trained models and even leads to negative transfer. Meanwhile, prompt tuning has seen emerging success in natural language processing by aligning pre-training and fine-tuning with consistent training objectives. In this paper, we identify the challenges for graph prompt tuning: The first is the lack of a strong and universal pre-training task across sundry pre-training methods in graph domain. The second challenge lies in the difficulty of designing a consistent training objective for both pre-training and downstream tasks. To overcome above obstacles, we propose a novel framework named SGL-PT which follows the learning strategy ``Pre-train, Prompt, and Predict''. Specifically, we raise a strong and universal pre-training task coined as SGL that acquires the complementary merits of generative and contrastive self-supervised graph learning. And aiming for graph classification task, we unify pre-training and fine-tuning by designing a novel verbalizer-free prompting function, which reformulates the downstream task in a similar format as pretext task. Empirical results show that our method surpasses other baselines under unsupervised setting, and our prompt tuning method can greatly facilitate models on biological datasets over fine-tuning methods.

Related papers

• HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks [24.435068514392487]
  HetGPT is a post-training prompting framework for graph neural networks. It improves the performance of state-of-the-art HGNNs on semi-supervised node classification.
  arXiv  Detail & Related papers  (2023-10-23T19:35:57Z)
• ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt [67.8934749027315]
  We propose a unified framework for graph hybrid pre-training which injects the task identification and position identification into GNNs. We also propose a novel pre-training paradigm based on a group of $k$-nearest neighbors.
  arXiv  Detail & Related papers  (2023-10-23T12:11:13Z)
• Deep Prompt Tuning for Graph Transformers [55.2480439325792]
  Fine-tuning is resource-intensive and requires storing multiple copies of large models. We propose a novel approach called deep graph prompt tuning as an alternative to fine-tuning. By freezing the pre-trained parameters and only updating the added tokens, our approach reduces the number of free parameters and eliminates the need for multiple model copies.
  arXiv  Detail & Related papers  (2023-09-18T20:12:17Z)
• SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning [131.04781590452308]
  We present SimTeG, a frustratingly Simple approach for Textual Graph learning. We first perform supervised parameter-efficient fine-tuning (PEFT) on a pre-trained LM on the downstream task. We then generate node embeddings using the last hidden states of finetuned LM.
  arXiv  Detail & Related papers  (2023-08-03T07:00:04Z)
• Rethinking Visual Prompt Learning as Masked Visual Token Modeling [106.71983630652323]
  We propose Visual Prompt learning as masked visual Token Modeling (VPTM) to transform the downstream visual classification into the pre-trained masked visual token prediction. VPTM is the first visual prompt method on the generative pre-trained visual model, which achieves consistency between pre-training and downstream visual classification by task reformulation.
  arXiv  Detail & Related papers  (2023-03-09T02:43:10Z)
• GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks [16.455234748896157]
  GraphPrompt is a novel pre-training and prompting framework on graphs. It unifies pre-training and downstream tasks into a common task template. It also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-train model.
  arXiv  Detail & Related papers  (2023-02-16T02:51:38Z)
• Self-supervised Graph Masking Pre-training for Graph-to-Text Generation [5.108327983929205]
  Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text (G2T) generation. We propose graph masking pre-training strategies that neither require supervision signals nor adjust the architecture of the underlying pre-trained encoder-decoder model. Our approach achieves new state-of-the-art results on WebNLG+ 2020 and EventNarrative G2T generation datasets.
  arXiv  Detail & Related papers  (2022-10-19T14:44:56Z)
• Self-Distillation for Further Pre-training of Transformers [83.84227016847096]
  We propose self-distillation as a regularization for a further pre-training stage. We empirically validate the efficacy of self-distillation on a variety of benchmark datasets for image and text classification tasks.
  arXiv  Detail & Related papers  (2022-09-30T02:25:12Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.