Multiplex Graph

Multiplex graphs represent complex systems with multiple interconnected layers or "views" of relationships between nodes, offering richer data representations than single-layer graphs. Current research focuses on developing effective unsupervised and self-supervised learning methods, often employing graph neural networks (GNNs) like Graph Attention Networks (GATs) and incorporating techniques like hierarchical aggregations and k-nearest neighbor augmentation to improve node embedding and clustering performance. These advancements are crucial for tackling real-world challenges in diverse fields, including bioinformatics, social network analysis, and disease prediction, where integrating information from multiple data sources is essential for accurate modeling and prediction.