MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs
  Embedding
Marco Bongiovanni Luca Gallo Roberto Grasso Alfredo Pulvirenti
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.