发表机构
Arizona State University; University of California, Riverside（亚利桑那州立大学; 加州大学河滨分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出TopoSIGN，一个基于拓扑引导的符号图预训练与提示学习框架，结合磁符号拉普拉斯编码器和持久同调分支，有效提取结构信息并提升迁移学习性能。
AI 中文摘要
符号图出现在信任-不信任网络、金融相关系统、生物相互作用图以及许多其他领域中，在这些领域中边可以是正的或负的，并且可能是有向的。虽然符号图神经网络改进了特定任务的学习，但符号图上的图迁移学习仍然发展不足。在本文中，我们介绍了TopoSIGN，一个开创性的基于拓扑引导的符号图预训练和提示学习框架。TopoSIGN结合了基于磁符号拉普拉斯算子的结构编码器和一个新颖的持久同调分支，该分支通过Dowker复形持久性图像来总结符号拓扑。融合后的嵌入随后被转移到提示学习函数中。在合成和真实世界数据集上的实验结果表明，TopoSIGN在提取符号图中有用结构信息方面的有效性，以及所提出的通用框架的适应性和灵活性。
英文摘要
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.
Comments26 pages, 3 figures, Accepted to Learning on Graphs Conference (LoG 2026)
