# SigGAN : Adversarial Model for Learning Signed Relationships in Networks

arXiv: https://arxiv.org/abs/2201.06437 · HTML: https://mlsanity.com/paper/2201.06437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 79 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 28 of 28)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [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)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](https://mlsanity.com/paper/1506.05751/) (2015)
- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://mlsanity.com/paper/1711.08267/) (2017)
- [Community Interaction and Conflict on the Web](https://mlsanity.com/paper/1803.03697/) (2018)
- [Signed Graph Attention Networks](https://mlsanity.com/paper/1906.10958/) (2019)
- [SDGNN: Learning Node Representation for Signed Directed Networks](https://mlsanity.com/paper/2101.02390/) (2021)
- [Exploiting Social Network Structure for Person-to-Person Sentiment Analysis](https://mlsanity.com/paper/1409.2450/) (2014)
- [A Survey of Signed Network Mining in Social Media](https://mlsanity.com/paper/1511.07569/) (2015)
- [SNE: Signed Network Embedding](https://mlsanity.com/paper/1703.04837/) (2017)
- [SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction](https://mlsanity.com/paper/1712.00732/) (2017)
- [Distributed Representations of Signed Networks](https://mlsanity.com/paper/1702.06819/) (2017)
- [Deep Network Embedding for Graph Representation Learning in Signed Networks](https://mlsanity.com/paper/1901.01718/) (2019)
- [Hyperbolic Node Embedding for Signed Networks](https://mlsanity.com/paper/1910.13090/) (2019)
- [Prediction and Clustering in Signed Networks: A Local to Global Perspective](https://mlsanity.com/paper/1302.5145/) (2013)
- [Negative Link Prediction in Social Media](https://mlsanity.com/paper/1412.2723/) (2014)
- [Statistical Link Label Modeling for Sign Prediction: Smoothing Sparsity by Joining Local and Global Information](https://mlsanity.com/paper/1802.06265/) (2018)
- [MUSE: Multi-faceted Attention for Signed Network Embedding](https://mlsanity.com/paper/2104.14449/) (2021)
- [Exploring Hierarchies in Online Social Networks](https://mlsanity.com/paper/1502.04220/) (2015)
- [Signed Link Analysis in Social Media Networks](https://mlsanity.com/paper/1603.06878/) (2016)
- [Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances](https://mlsanity.com/paper/1806.03577/) (2018)
- [Detecting Antagonistic and Allied Communities on Social Media](https://mlsanity.com/paper/1807.03617/) (2018)

## Built on by (most cited first, 1 of 1)

- [Balancing Augmentation with Edge-Utility Filter for Signed GNNs](https://mlsanity.com/paper/2310.16862/) (2023)

## Abstract (arXiv)

Signed link prediction in graphs is an important problem that has applications in diverse domains. It is a binary classification problem that predicts whether an edge between a pair of nodes is positive or negative. Existing approaches for link prediction in unsigned networks cannot be directly applied for signed link prediction due to their inherent differences. Further, additional structural constraints, like, the structural balance property of the signed networks must be considered for signed link prediction. Recent signed link prediction approaches generate node representations using either generative models or discriminative models. Inspired by the recent success of Generative Adversarial Network (GAN) based models which comprises of a discriminator and generator in several applications, we propose a Generative Adversarial Network (GAN) based model for signed networks, SigGAN. It considers the requirements of signed networks, such as, integration of information from negative edges, high imbalance in number of positive and negative edges and structural balance theory. Comparing the performance with state of the art techniques on several real-world datasets validates the effectiveness of SigGAN.