Graph representation learning has rapidly emerged as a pivotal field of
study. Despite its growing popularity, the majority of research has been
confined to embedding single-layer graphs, which fall short in representing
complex systems with multifaceted relationships. To bridge this gap, we
introduce MPXGAT, an innovative attention-based deep learning model tailored to
multiplex graph embedding. Leveraging the robustness of Graph Attention
Networks (GATs), MPXGAT captures the structure of multiplex networks by
harnessing both intra-layer and inter-layer connections. This exploitation
facilitates accurate link prediction within and across the network's multiple
layers. Our comprehensive experimental evaluation, conducted on various
benchmark datasets, confirms that MPXGAT consistently outperforms
state-of-the-art competing algorithms.

MPXGAT 是一种基于注意力机制的深度学习模型，专门用于多重图嵌入，通过利用图注意力网络（GATs）的鲁棒性，它捕捉多重网络的结构，从而促进了网络多个层内和层间的准确链路预测。我们在各种基准数据集上进行的全面实验评估证实，MPXGAT 始终优于最先进的竞争算法。