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TopoSIGN addresses a critical gap in graph neural networks by introducing a pre-training framework specifically designed for signed graphs, where edge relationships can be positive or negative. By integrating structural encoders with persistent homology, the method effectively transfers learned embeddings to downstream prompt learning tasks across diverse domains like finance and biology. This research advances the state-of-the-art in handling complex relational data structures within machine learning pipelines.
Signed Graph Pre-Training and Prompt Learning
arXiv:2609.25722v1 Announce Type: new 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.
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.
Machine Learning (cs.LG)
arXiv:2609.25722 [cs.LG]
(or arXiv:2609.25722v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.25722 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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