2024/03/28 by Bongiovanni, Marco, Gallo, Luca, Grasso, Roberto +1
#Discrete Mathematics (cs.DM) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Social and Information Networks (cs.SI)
paper · doi:10.48550/arxiv.2403.19246
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.