Signed Graph Pre-Training and Prompt Learning

2026-09-22

AI Summary

本文针对有符号图的迁移学习问题，提出了TopoSIGN框架，结合结构编码器和持久同调分支来提取和利用有符号图的拓扑信息。

Abstract

Signed graphs arise in trust--distrust networks, financial correlation systems, biological interaction graphs, and many other domains in which edges can be positive or negative and may also be directed. While signed graph neural networks have improved task-specific learning, graph transfer learning on signed graphs remains underdeveloped. In this paper, we introduce TopoSIGN, a pioneer topology-guided graph pre-training and prompt learning framework for signed graphs. TopoSIGN combines a structural encoder built on the magnetic signed Laplacian with a novel persistent-homology branch that summarizes signed topology through Dowker-complex persistence images. The fused embeddings are then transferred to a prompt learning function. Experimental results on synthetic and real-world datasets demonstrate the efficacy of TopoSIGN in extracting useful structural information in signed graphs, as well as the adaptability and flexibility of the proposed general framework.

Problem

• signed graphs
• graph transfer learning
• topology

Innovation

• signed graph
• pre-training
• prompt learning
• topology-guided
• persistent homology