# Signed Graph Representation Learning: A Survey

arXiv: https://arxiv.org/abs/2402.15980 · HTML: https://mlsanity.com/paper/2402.15980/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2024.

## Built on by (most cited first, 7 of 7)

- [SGPT: Few-Shot Prompt Tuning for Signed Graphs](https://mlsanity.com/paper/2412.12155/) (2024)
- [GegenNet: Spectral Convolutional Neural Networks for Link Sign Prediction in Signed Bipartite Graphs](https://mlsanity.com/paper/2508.19907/) (2025)
- [Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social Balance](https://mlsanity.com/paper/2407.15643/) (2024)
- [Belief Is All You Need: Modeling Narrative Archetypes in Conspiratorial Discourse](https://mlsanity.com/paper/2512.10105/) (2025)
- [TAS-GNN: A Status-Aware Signed Graph Neural Network for Anomaly Detection in Bitcoin Trust Systems](https://mlsanity.com/paper/2603.13290/) (2026)
- [Retrieval Is Not Enough: Why Organizational AI Needs Epistemic Infrastructure](https://mlsanity.com/paper/2604.11759/) (2026)
- [Signed Graph Pre-Training and Prompt Learning](https://mlsanity.com/paper/2609.25722/) (2026)

## Abstract (arXiv)

With the prevalence of social media, the connectedness between people has been greatly enhanced. Real-world relations between users on social media are often not limited to expressing positive ties such as friendship, trust, and agreement, but they also reflect negative ties such as enmity, mistrust, and disagreement, which can be well modelled by signed graphs. Signed Graph Representation Learning (SGRL) is an effective approach to analyze the complex patterns in real-world signed graphs with the co-existence of positive and negative links. In recent years, SGRL has witnesses fruitful results. SGRL tries to allocate low-dimensional representations to nodes and edges which could preserve the graph structure, attribute and some collective properties, e.g., balance theory and status theory. To the best of knowledge, there is no survey paper about SGRL up to now. In this paper, we present a broad review of SGRL methods and discuss some future research directions.