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

• URL: http://arxiv.org/abs/2403.19246v1
• Date: Thu, 28 Mar 2024 09:06:23 GMT
• Title: MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
• Authors: Marco Bongiovanni, Luca Gallo, Roberto Grasso, Alfredo Pulvirenti,
• Abstract summary: We introduce MPXGAT, an innovative attention-based deep learning model tailored to multiplex graph embedding. MPXGAT captures the structure of multiplex networks by harnessing both intra-layer and inter-layer connections. Our comprehensive experimental evaluation, conducted on various benchmark datasets, confirms that MPXGAT consistently outperforms state-of-the-art competing algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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