Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective

• URL: http://arxiv.org/abs/2402.13556v1
• Date: Wed, 21 Feb 2024 06:25:54 GMT
• Title: Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
• Authors: Yuchen Yan, Peiyan Zhang, Zheng Fang, Qingqing Long
• Abstract summary: "Graph pre-training and fine-tuning" paradigm has significantly improved Graph Neural Networks(GNNs) However, due to the immense gap of data and tasks between the pre-training and fine-tuning stages, the model performance is still limited. We propose a novel graph prompt based method called Inductive Graph Alignment Prompt(IGAP)
• Score: 13.277779426525056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The "Graph pre-training and fine-tuning" paradigm has significantly improved Graph Neural Networks(GNNs) by capturing general knowledge without manual annotations for downstream tasks. However, due to the immense gap of data and tasks between the pre-training and fine-tuning stages, the model performance is still limited. Inspired by prompt fine-tuning in Natural Language Processing(NLP), many endeavors have been made to bridge the gap in graph domain. But existing methods simply reformulate the form of fine-tuning tasks to the pre-training ones. With the premise that the pre-training graphs are compatible with the fine-tuning ones, these methods typically operate in transductive setting. In order to generalize graph pre-training to inductive scenario where the fine-tuning graphs might significantly differ from pre-training ones, we propose a novel graph prompt based method called Inductive Graph Alignment Prompt(IGAP). Firstly, we unify the mainstream graph pre-training frameworks and analyze the essence of graph pre-training from graph spectral theory. Then we identify the two sources of the data gap in inductive setting: (i) graph signal gap and (ii) graph structure gap. Based on the insight of graph pre-training, we propose to bridge the graph signal gap and the graph structure gap with learnable prompts in the spectral space. A theoretical analysis ensures the effectiveness of our method. At last, we conduct extensive experiments among nodes classification and graph classification tasks under the transductive, semi-inductive and inductive settings. The results demonstrate that our proposed method can successfully bridge the data gap under different settings.

Related papers

• Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis [7.309233340654514]
  This paper introduces a theoretical framework that rigorously analyzes graph prompting from a data operation perspective. We provide a formal guarantee theorem, demonstrating graph prompts capacity to approximate graph transformation operators. We derive upper bounds on the error of these data operations by graph prompts for a single graph and extend this discussion to batches of graphs.
  arXiv  Detail & Related papers  (2024-10-02T15:07:13Z)
• Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns [13.378277755978258]
  We show that the structural divergence between pre-training and downstream graphs significantly limits the transferability when using the vanilla fine-tuning strategy. We propose G-Tuning to preserve the generative patterns of downstream graphs. G-Tuning demonstrates an average improvement of 0.5% and 2.6% on in-domain and out-of-domain transfer learning experiments.
  arXiv  Detail & Related papers  (2023-12-21T05:17:10Z)
• Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks [10.794305560114903]
  Self-Prompt is a prompting framework for graphs based on the model and data itself. We introduce asymmetric graph contrastive learning for pretext to address heterophily and align the objectives of pretext and downstream tasks. We conduct extensive experiments on 11 benchmark datasets to demonstrate its superiority.
  arXiv  Detail & Related papers  (2023-10-16T12:58:04Z)
• 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)
• You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction [79.15394378571132]
  We investigate a previously overlooked phenomenon: in many cases, a densely connected, complementary graph can be found for the original graph. The denser graph may share nodes with the original graph, which offers a natural bridge for transferring selective, meaningful knowledge. We identify this setting as Graph Intersection-induced Transfer Learning (GITL), which is motivated by practical applications in e-commerce or academic co-authorship predictions.
  arXiv  Detail & Related papers  (2023-02-27T22:56:06Z)
• Spectral Augmentations for Graph Contrastive Learning [50.149996923976836]
  Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learning for pre-training. We propose a set of well-motivated graph transformation operations to provide a bank of candidates when constructing augmentations for a graph contrastive objective.
  arXiv  Detail & Related papers  (2023-02-06T16:26:29Z)
• MentorGNN: Deriving Curriculum for Pre-Training GNNs [61.97574489259085]
  We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs. We shed new light on the problem of domain adaption on relational data (i.e., graphs) by deriving a natural and interpretable upper bound on the generalization error of the pre-trained GNNs.
  arXiv  Detail & Related papers  (2022-08-21T15:12:08Z)
• 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)
• Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations [94.41860307845812]
  Self-supervision is recently surging at its new frontier of graph learning. GraphCL uses a prefabricated prior reflected by the ad-hoc manual selection of graph data augmentations. We have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators. We have leveraged both principles of information minimization (InfoMin) and information bottleneck (InfoBN) to regularize the learned priors.
  arXiv  Detail & Related papers  (2022-01-04T15:49:18Z)

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.