# Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling

arXiv: https://arxiv.org/abs/2411.07482 · HTML: https://mlsanity.com/paper/2411.07482/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [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)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Nested Graph Neural Networks](https://mlsanity.com/paper/2110.13197/) (2021)
- [Edge Contraction Pooling for Graph Neural Networks](https://mlsanity.com/paper/1905.10990/) (2019)
- [SHAPNN: Shapley Value Regularized Tabular Neural Network](https://mlsanity.com/paper/2309.08799/) (2023)

## Built on by (most cited first, 5 of 5)

- [Comparative Analysis of Pooling Mechanisms in LLMs: A Sentiment Analysis Perspective](https://mlsanity.com/paper/2411.14654/) (2024)
- [Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning](https://mlsanity.com/paper/2412.17271/) (2024)
- [FGATT: A Robust Framework for Wireless Data Imputation Using Fuzzy Graph Attention Networks and Transformer Encoders](https://mlsanity.com/paper/2412.01979/) (2024)
- [RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations](https://mlsanity.com/paper/2509.06286/) (2025)
- [TAS-GNN: A Status-Aware Signed Graph Neural Network for Anomaly Detection in Bitcoin Trust Systems](https://mlsanity.com/paper/2603.13290/) (2026)

## Abstract (arXiv)

Link prediction is crucial for understanding complex networks but traditional Graph Neural Networks (GNNs) often rely on random negative sampling, leading to suboptimal performance. This paper introduces Fuzzy Graph Attention Networks (FGAT), a novel approach integrating fuzzy rough sets for dynamic negative sampling and enhanced node feature aggregation. Fuzzy Negative Sampling (FNS) systematically selects high-quality negative edges based on fuzzy similarities, improving training efficiency. FGAT layer incorporates fuzzy rough set principles, enabling robust and discriminative node representations. Experiments on two research collaboration networks demonstrate FGAT's superior link prediction accuracy, outperforming state-of-the-art baselines by leveraging the power of fuzzy rough sets for effective negative sampling and node feature learning.