# GP2F: Cross-Domain Graph Prompt Learning and Adaptive Fusion Framework with Pre-trained Graph Neural Networks

> This article provides an in-depth analysis of the GP2F framework, an innovative method proposed by the Tianjin University team. It addresses the domain gap issue in the transfer of pre-trained graph neural networks (GNNs) to downstream tasks through cross-domain graph prompt learning and an adaptive fusion mechanism. This work has been accepted by ICML 2026.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-20T06:11:46.000Z
- 最近活动: 2026-05-20T06:18:47.379Z
- 热度: 148.9
- 关键词: graph neural networks, transfer learning, cross-domain, prompt learning, ICML 2026, pre-training, adaptive fusion
- 页面链接: https://www.zingnex.cn/en/forum/thread/gp2f
- Canonical: https://www.zingnex.cn/forum/thread/gp2f
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## GP2F Framework: Core Overview

# GP2F: Cross-Domain Graph Prompting with Adaptive Fusion Framework

This work, proposed by Tianjin University team, addresses the domain gap issue in pre-trained GNN transfer via cross-domain graph prompt learning and adaptive fusion. It has been accepted by ICML 2026.

## Research Background & Motivation

Graph Neural Networks (GNNs) excel at graph data but face labeled data scarcity. Pre-trained GNNs offer a solution, yet domain gaps between pre-training and downstream tasks limit transfer. Existing methods (fine-tuning, prompting) struggle with cross-domain scenarios like molecular graphs, social networks, and knowledge graphs due to structural and task differences.

## GP2F Core Components

GP2F's core idea is to use learnable graph prompts for domain adaptation and adaptive fusion of multiple pre-trained models.

1. **Cross-domain Graph Prompt Learning**: Adds learnable prompt nodes/edges to input graphs, optimizing to bridge domain gaps without task labels. It's parameter-efficient, preserving pre-trained knowledge.

2. **Adaptive Fusion**: Dynamically adjusts weights of pre-trained models based on input data characteristics, selecting optimal knowledge combinations for each sample.

## Technical Implementation Details

Built on PyTorch and PyTorch Geometric, GP2F includes:
- **Prompt Encoder**: Combines raw graphs with learnable prompts.
- **Pre-trained Model Pool**: Supports GCN, GAT, GraphSAGE, etc.
- **Fusion Network**: Adaptive weighted fusion of model outputs.
- **Task Head**: Task-specific prediction module.

Training uses two stages: first optimize prompt parameters, then jointly optimize prompts and fusion weights to avoid overfitting.

## Experimental Validation Results

Evaluated on cross-domain benchmarks:
1. Outperforms traditional fine-tuning in cross-domain scenarios (e.g., pre-trained on molecular graphs, downstream social network tasks).
2. Adaptive fusion beats single-model prompting and simple average.
3. Parameter efficiency shines in few-shot learning, achieving near full-supervision performance with limited labels.

Accepted by ICML 2026, reflecting academic recognition.

## Application Prospects & Insights

Applicable to molecular computing, social networks, recommendation systems, drug discovery, and knowledge graph reasoning.

Insights:
- Input-level adaptation (prompt-first) balances efficiency and transfer effect better than forced fine-tuning.
- Adaptive fusion of pre-trained models is more effective than simple ensemble methods.

## Conclusion

GP2F represents a key advance in GNN transfer learning. Its cross-domain prompt learning and adaptive fusion address domain gaps effectively. As graph learning applications expand, such flexible domain-adaptive methods will grow in importance.