# SNE: Signed Network Embedding

arXiv: https://arxiv.org/abs/1703.04837 · HTML: https://mlsanity.com/paper/1703.04837/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2017.

## Built on by (most cited first, 20 of 20)

- [SDGNN: Learning Node Representation for Signed Directed Networks](https://mlsanity.com/paper/2101.02390/) (2021)
- [Signed Bipartite Graph Neural Networks](https://mlsanity.com/paper/2108.09638/) (2021)
- [PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs](https://mlsanity.com/paper/2202.10793/) (2022)
- [POLE: Polarized Embedding for Signed Networks](https://mlsanity.com/paper/2110.09899/) (2021)
- [SiReN: Sign-Aware Recommendation Using Graph Neural Networks](https://mlsanity.com/paper/2108.08735/) (2021)
- [Learning Stance Embeddings from Signed Social Graphs](https://mlsanity.com/paper/2201.11675/) (2022)
- [Self-Explainable Graph Transformer for Link Sign Prediction](https://mlsanity.com/paper/2408.08754/) (2024)
- [Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings](https://mlsanity.com/paper/2409.10452/) (2024)
- [MUSE: Multi-faceted Attention for Signed Network Embedding](https://mlsanity.com/paper/2104.14449/) (2021)
- [Exact Community Recovery over Signed Graphs](https://mlsanity.com/paper/2202.12255/) (2022)
- [DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks](https://mlsanity.com/paper/2409.19620/) (2024)
- [A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign Prediction](https://mlsanity.com/paper/2305.09869/) (2023)
- [Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions](https://mlsanity.com/paper/2307.03077/) (2023)
- [Mitigating Degree Bias in Signed Graph Neural Networks](https://mlsanity.com/paper/2408.08508/) (2024)
- [Survey of Generative Methods for Social Media Analysis](https://mlsanity.com/paper/2112.07041/) (2021)
- [SigGAN : Adversarial Model for Learning Signed Relationships in Networks](https://mlsanity.com/paper/2201.06437/) (2022)
- [Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding](https://mlsanity.com/paper/2309.00816/) (2023)
- [Whole-Graph Representation Learning For the Classification of Signed Networks](https://mlsanity.com/paper/2409.20073/) (2024)
- [SGA: A Graph Augmentation Method for Signed Graph Neural Networks](https://mlsanity.com/paper/2310.09705/) (2023)
- [Enhancing Signed Graph Neural Networks through Curriculum-Based Training](https://mlsanity.com/paper/2310.11083/) (2023)

## Abstract (arXiv)

Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.