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

arXiv:2403.19246v1 Announce Type: new
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 …

abstract arxiv attention bridge complex systems cs.dm cs.lg cs.si deep learning embedding gap graph graph representation graphs layer pivotal relationships representation representation learning research study systems type