# SGPT: Few-Shot Prompt Tuning for Signed Graphs

arXiv: https://arxiv.org/abs/2412.12155 · HTML: https://mlsanity.com/paper/2412.12155/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Graph Contrastive Learning Automated](https://mlsanity.com/paper/2106.07594/) (2021)
- [GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://mlsanity.com/paper/2302.08043/) (2023)
- [All in One: Multi-task Prompting for Graph Neural Networks](https://mlsanity.com/paper/2307.01504/) (2023)
- [Signed Graph Attention Networks](https://mlsanity.com/paper/1906.10958/) (2019)
- [HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning](https://mlsanity.com/paper/2312.01878/) (2023)
- [SDGNN: Learning Node Representation for Signed Directed Networks](https://mlsanity.com/paper/2101.02390/) (2021)
- [Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning](https://mlsanity.com/paper/2410.14211/) (2024)
- [Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs](https://mlsanity.com/paper/2311.15317/) (2023)
- [Unleashing the Power of Visual Prompting At the Pixel Level](https://mlsanity.com/paper/2212.10556/) (2022)
- [HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2310.15318/) (2023)
- [A Survey of Signed Network Mining in Social Media](https://mlsanity.com/paper/1511.07569/) (2015)
- [AdapterGNN: Parameter-Efficient Fine-Tuning Improves Generalization in GNNs](https://mlsanity.com/paper/2304.09595/) (2023)
- [Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner](https://mlsanity.com/paper/2305.01711/) (2023)
- [VEWS: A Wikipedia Vandal Early Warning System](https://mlsanity.com/paper/1507.01272/) (2015)
- [Signed Graph Representation Learning: A Survey](https://mlsanity.com/paper/2402.15980/) (2024)
- [Efficient Influence Minimization via Node Blocking](https://mlsanity.com/paper/2405.12871/) (2024)

## Built on by (most cited first, 6 of 6)

- [HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning](https://mlsanity.com/paper/2505.17464/) (2025)
- [Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning](https://mlsanity.com/paper/2508.06588/) (2025)
- [PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.12434/) (2025)
- [Beyond Homophily: Community Search on Heterophilic Graphs](https://mlsanity.com/paper/2601.01703/) (2026)
- [Anchor-guided Hypergraph Condensation with Dual-level Discrimination](https://mlsanity.com/paper/2605.10001/) (2026)
- [Signed Graph Pre-Training and Prompt Learning](https://mlsanity.com/paper/2609.25722/) (2026)

## Abstract (arXiv)

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.