MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding

Marco Bongiovanni, Luca Gallo, Roberto Grasso, Alfredo Pulvirenti
March, 2024
Cite Project Preprint GitHub

Abstract

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.

Type
Preprint
Publication
arXiv
De-anonymization Link predictions Online social networks Graph neural networks Graph attention networks Graph embedding Multiplex networks
Luca Gallo
Luca Gallo
Postdoctoral research fellow

My research focuses on applying network science to studying the structure and dynamics of social systems.