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Computer Science > Machine Learning
Title: MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
(Submitted on 28 Mar 2024)
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.
Submission history
From: Marco Bongiovanni [view email][v1] Thu, 28 Mar 2024 09:06:23 GMT (590kb,D)
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