# SIGformer: Sign-aware Graph Transformer for Recommendation

arXiv: https://arxiv.org/abs/2404.11982 · HTML: https://mlsanity.com/paper/2404.11982/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2024.

## Built on by (most cited first, 12 of 12)

- [A Survey of Graph Transformers: Architectures, Theories and Applications](https://mlsanity.com/paper/2502.16533/) (2025)
- [Self-Explainable Graph Transformer for Link Sign Prediction](https://mlsanity.com/paper/2408.08754/) (2024)
- [A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation](https://mlsanity.com/paper/2407.19886/) (2024)
- [Uncertainty-Aware Graph Structure Learning](https://mlsanity.com/paper/2502.12618/) (2025)
- [You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control](https://mlsanity.com/paper/2507.16829/) (2025)
- [Hgformer: Hyperbolic Graph Transformer for Recommendation](https://mlsanity.com/paper/2502.15693/) (2024)
- [A Scalable Inter-edge Correlation Modeling in CopulaGNN for Link Sign Prediction](https://mlsanity.com/paper/2601.19175/) (2026)
- [Talos: Optimizing Top-K Accuracy in Recommender Systems](https://mlsanity.com/paper/2601.19276/) (2026)
- [Signed-Graph Recommendation as Structural Consistency Maximization](https://mlsanity.com/paper/2607.05952/) (2026)
- [HeSRN: Representation Learning On Heterogeneous Graphs via Slot-Aware Retentive Network](https://mlsanity.com/paper/2510.09767/) (2025)
- [Learning over Positive and Negative Edges with Contrastive Message Passing](https://mlsanity.com/paper/2605.17854/) (2026)
- [The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders](https://mlsanity.com/paper/2606.21911/) (2026)

## Abstract (arXiv)

In recommender systems, most graph-based methods focus on positive user feedback, while overlooking the valuable negative feedback. Integrating both positive and negative feedback to form a signed graph can lead to a more comprehensive understanding of user preferences. However, the existing efforts to incorporate both types of feedback are sparse and face two main limitations: 1) They process positive and negative feedback separately, which fails to holistically leverage the collaborative information within the signed graph; 2) They rely on MLPs or GNNs for information extraction from negative feedback, which may not be effective. To overcome these limitations, we introduce SIGformer, a new method that employs the transformer architecture to sign-aware graph-based recommendation. SIGformer incorporates two innovative positional encodings that capture the spectral properties and path patterns of the signed graph, enabling the full exploitation of the entire graph. Our extensive experiments across five real-world datasets demonstrate the superiority of SIGformer over state-of-the-art methods. The code is available at https://github.com/StupidThree/SIGformer.