SGPT: Few-Shot Prompt Tuning for Signed Graphs

• URL: http://arxiv.org/abs/2412.12155v2
• Date: Sun, 17 Aug 2025 16:07:53 GMT
• Title: SGPT: Few-Shot Prompt Tuning for Signed Graphs
• Authors: Zian Zhai, Sima Qing, Xiaoyang Wang, Wenjie Zhang,
• Abstract summary: Signed Graph Prompt Tuning (SGPT) is a graph prompting framework that adapts pre-trained unsigned GNNs to few-shot signed graph tasks.<n>We conduct experiments on seven benchmark signed graph datasets, demonstrating that SGPT significantly outperforms existing state-of-the-art methods.
• Score: 8.42756062274768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Signed Graph Neural Networks (SGNNs) are effective in learning expressive representations for signed graphs but typically require substantial task-specific labels, limiting their applicability in label-scarce industrial scenarios. In contrast, unsigned graph structures are abundant and can be readily leveraged to pre-train Graph Neural Networks (GNNs), offering a promising solution to reduce supervision requirements in downstream signed graph tasks. However, transferring knowledge from unsigned to signed graphs is non-trivial due to the fundamental discrepancies in graph types and task objectives between pre-training and downstream phases. To address this challenge, we propose Signed Graph Prompt Tuning (SGPT), a novel graph prompting framework that adapts pre-trained unsigned GNNs to few-shot signed graph tasks. We first design a graph template based on balance theory to disentangle mixed node relationships introduced by negative links, mitigating the structural mismatches between unsigned and signed graphs. We further introduce a task template that reformulates downstream signed tasks into a unified link prediction objective, aligning their optimization goals with the pre-training task. Furthermore, we develop feature prompts that align downstream semantic spaces with the feature spaces learned during pre-training, and semantic prompts to integrate link sign semantics in a task-aware manner. We conduct extensive experiments on seven benchmark signed graph datasets, demonstrating that SGPT significantly outperforms existing state-of-the-art methods, establishing a powerful and generalizable solution for few-shot signed graph learning.

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