MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909154179809280 |
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| author | Bongiovanni, Marco Gallo, Luca Grasso, Roberto Pulvirenti, Alfredo |
| author_facet | Bongiovanni, Marco Gallo, Luca Grasso, Roberto Pulvirenti, Alfredo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19246 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding Bongiovanni, Marco Gallo, Luca Grasso, Roberto Pulvirenti, Alfredo Machine Learning Discrete Mathematics Social and Information Networks 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. |
| title | MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding |
| topic | Machine Learning Discrete Mathematics Social and Information Networks |
| url | https://arxiv.org/abs/2403.19246 |