MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
<|reference_start|>MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding: 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.<|reference_end|>
@article{bongiovanni2024mpxgat:,
    title={MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs
  Embedding},
    author={Marco Bongiovanni, Luca Gallo, Roberto Grasso, Alfredo Pulvirenti},
    journal={arXiv preprint arXiv:2403.19246},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19246},
    primaryClass={cs.LG cs.DM cs.SI}
}