通过模糊图注意网络和动态负采样增强链接预测

Nov, 2024

通过模糊图注意网络和动态负采样增强链接预测

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

Jinming Xing

TL;DR本研究解决了传统图神经网络在链接预测中随机负采样导致的性能不足问题。提出了模糊图注意网络（FGAT），结合模糊粗集进行动态负采样和节点特征聚合，显著提升了训练效率和准确性。实验表明，FGAT在研究合作网络中的链接预测精度优于现有的先进方法，具有重要的潜在影响。

Abstract

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 Att