# Balancing Augmentation with Edge-Utility Filter for Signed GNNs

arXiv: https://arxiv.org/abs/2310.16862 · HTML: https://mlsanity.com/paper/2310.16862/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [Data Augmentation for Graph Neural Networks](https://mlsanity.com/paper/2006.06830/) (2020)
- [Large-Scale Representation Learning on Graphs via Bootstrapping](https://mlsanity.com/paper/2102.06514/) (2021)
- [G-Mixup: Graph Data Augmentation for Graph Classification](https://mlsanity.com/paper/2202.07179/) (2022)
- [DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks](https://mlsanity.com/paper/2111.06283/) (2021)
- [Signed Graph Attention Networks](https://mlsanity.com/paper/1906.10958/) (2019)
- [Spectral Augmentation for Self-Supervised Learning on Graphs](https://mlsanity.com/paper/2210.00643/) (2022)
- [SSSNET: Semi-Supervised Signed Network Clustering](https://mlsanity.com/paper/2110.06623/) (2021)
- [Signed Bipartite Graph Neural Networks](https://mlsanity.com/paper/2108.09638/) (2021)
- [Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples](https://mlsanity.com/paper/2210.00728/) (2022)
- [RobustECD: Enhancement of Network Structure for Robust Community Detection](https://mlsanity.com/paper/1911.01670/) (2019)
- [Dual-channel Early Warning Framework for Ethereum Ponzi Schemes](https://mlsanity.com/paper/2206.07895/) (2022)
- [Negative Link Prediction in Social Media](https://mlsanity.com/paper/1412.2723/) (2014)
- [SigGAN : Adversarial Model for Learning Signed Relationships in Networks](https://mlsanity.com/paper/2201.06437/) (2022)

## Abstract (arXiv)

Signed graph neural networks (SGNNs) has recently drawn more attention as many real-world networks are signed networks containing two types of edges: positive and negative. The existence of negative edges affects the SGNN robustness on two aspects. One is the semantic imbalance as the negative edges are usually hard to obtain though they can provide potentially useful information. The other is the structural unbalance, e.g. unbalanced triangles, an indication of incompatible relationship among nodes. In this paper, we propose a balancing augmentation method to address the above two aspects for SGNNs. Firstly, the utility of each negative edge is measured by calculating its occurrence in unbalanced structures. Secondly, the original signed graph is selectively augmented with the use of (1) an edge perturbation regulator to balance the number of positive and negative edges and to determine the ratio of perturbed edges to original edges and (2) an edge utility filter to remove the negative edges with low utility to make the graph structure more balanced. Finally, a SGNN is trained on the augmented graph which effectively explores the credible relationships. A detailed theoretical analysis is also conducted to prove the effectiveness of each module. Experiments on five real-world datasets in link prediction demonstrate that our method has the advantages of effectiveness and generalization and can significantly improve the performance of SGNN backbones.