Unified Graph Prompt Learning via Low-Rank Graph Message Prompting

• URL: http://arxiv.org/abs/2604.11257v1
• Date: Mon, 13 Apr 2026 10:07:12 GMT
• Title: Unified Graph Prompt Learning via Low-Rank Graph Message Prompting
• Abstract summary: Graph Data Prompt (GDP) introduces specific prompts in graph data for efficiently adapting pre-trained GNNs.<n>GDPs have been respectively designed for distinct graph component (e.g., node features, edge features, edge weights) and thus operate within limited prompt spaces for graph data.<n>We propose Low-Rank GMP (LR-GMP) which leverages low-rank prompt representation to achieve an effective and compact graph prompt learning.
• Score: 19.25560625766155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Data Prompt (GDP), which introduces specific prompts in graph data for efficiently adapting pre-trained GNNs, has become a mainstream approach to graph fine-tuning learning problem. However, existing GDPs have been respectively designed for distinct graph component (e.g., node features, edge features, edge weights) and thus operate within limited prompt spaces for graph data. To the best of our knowledge, it still lacks a unified prompter suitable for targeting all graph components simultaneously. To address this challenge, in this paper, we first propose to reinterpret a wide range of existing GDPs from an aspect of Graph Message Prompt (GMP) paradigm. Based on GMP, we then introduce a novel graph prompt learning approach, termed Low-Rank GMP (LR-GMP), which leverages low-rank prompt representation to achieve an effective and compact graph prompt learning. Unlike traditional GDPs that target distinct graph components separately, LR-GMP concurrently performs prompting on all graph components in a unified manner, thereby achieving significantly superior generalization and robustness on diverse downstream tasks. Extensive experiments on several graph benchmark datasets demonstrate the effectiveness and advantages of our proposed LR-GMP.

Related papers

• Zero-shot Graph Reasoning via Retrieval Augmented Framework with LLMs [15.558119182035995]
  We propose a new, training-free method, Graph Reasoning via Retrieval Augmented Framework (GRRAF)<n> GRRAF harnesses retrieval-augmented generation (RAG) alongside the code-generation capabilities of large language models (LLMs) to address a wide range of graph reasoning tasks.<n> Experimental evaluations on the GraphInstruct dataset reveal that GRRAF achieves 100% accuracy on most graph reasoning tasks.
  arXiv  Detail & Related papers  (2025-09-16T06:58:58Z)
• Beyond Message Passing: Neural Graph Pattern Machine [50.78679002846741]
  We introduce the Neural Graph Pattern Machine (GPM), a novel framework that bypasses message passing by learning directly from graph substructures.<n>GPM efficiently extracts, encodes, and prioritizes task-relevant graph patterns, offering greater expressivity and improved ability to capture long-range dependencies.
  arXiv  Detail & Related papers  (2025-01-30T20:37:47Z)
• Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
  We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs)<n>This framework provides a standardized setting to evaluate GNNs across diverse datasets.<n>We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-01T08:48:53Z)
• Instance-Aware Graph Prompt Learning [71.26108600288308]
  We introduce Instance-Aware Graph Prompt Learning (IA-GPL) in this paper. The process involves generating intermediate prompts for each instance using a lightweight architecture. Experiments conducted on multiple datasets and settings showcase the superior performance of IA-GPL compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-11-26T18:38:38Z)
• Reliable and Compact Graph Fine-tuning via GraphSparse Prompting [24.381392096420402]
  We propose exploiting sparse representation theory for graph prompting and present Graph Sparse Prompting (GSP) GSP aims to adaptively and sparsely select the optimal elements to achieve compact prompting for downstream tasks. A simple yet effective algorithm has been designed for solving GSFP and GSmFP models.
  arXiv  Detail & Related papers  (2024-10-29T05:31:14Z)
• A Unified Graph Selective Prompt Learning for Graph Neural Networks [20.595782116049428]
  Graph Prompt Feature (GPF) has achieved remarkable success in adapting pre-trained models for Graph Neural Networks (GNNs) We propose a new unified Graph Selective Prompt Feature learning (GSPF) for GNN fine-tuning.
  arXiv  Detail & Related papers  (2024-06-15T04:36:40Z)
• Learning on Large Graphs using Intersecting Communities [13.053266613831447]
  MPNNs iteratively update each node's representation in an input graph by aggregating messages from the node's neighbors.<n>MPNNs might quickly become prohibitive for large graphs provided they are not very sparse.<n>We propose approximating the input graph as an intersecting community graph (ICG) -- a combination of intersecting cliques.
  arXiv  Detail & Related papers  (2024-05-31T09:26:26Z)
• G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [61.93058781222079]
  We develop a flexible question-answering framework targeting real-world textual graphs. We introduce the first retrieval-augmented generation (RAG) approach for general textual graphs. G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem.
  arXiv  Detail & Related papers  (2024-02-12T13:13:04Z)
• MGNet: Learning Correspondences via Multiple Graphs [78.0117352211091]
  Learning correspondences aims to find correct correspondences from the initial correspondence set with an uneven correspondence distribution and a low inlier rate. Recent advances usually use graph neural networks (GNNs) to build a single type of graph or stack local graphs into the global one to complete the task. We propose MGNet to effectively combine multiple complementary graphs.
  arXiv  Detail & Related papers  (2024-01-10T07:58:44Z)
• GraphGPT: Graph Instruction Tuning for Large Language Models [27.036935149004726]
  Graph Neural Networks (GNNs) have evolved to understand graph structures. To enhance robustness, self-supervised learning (SSL) has become a vital tool for data augmentation. Our research tackles this by advancing graph model generalization in zero-shot learning environments.
  arXiv  Detail & Related papers  (2023-10-19T06:17:46Z)
• 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)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)

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.