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Arxiv
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106
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HGMP:Heterogeneous Graph Multi-Task Prompt Learning
- A new research paper introduces HGMP, a multi-task prompt learning framework for heterogeneous graph neural networks.
- HGMP aims to improve performance in downstream tasks by reformulating them into a unified graph-level task format, addressing model-task mismatch.
- The framework includes a graph-level contrastive pre-training strategy to leverage heterogeneous information and heterogeneous feature prompts for enhanced performance.
- Experimental results demonstrate that HGMP outperforms baseline methods on various tasks, showcasing its adaptability and effectiveness in the heterogeneous graph domain.
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