A Comprehensive Survey on Multi-Layer Graph Embedding Methods
Nooshin Yousefzadeh, My T. Thai, Sanjay Ranka · Vietnam Journal of Computer Science · 2025
The use of graphs enables the systematic modeling, analysis, and optimization of complex systems in various real-world domains. When multiple types of relationships or interactions exist among entities, whether homogeneous or heterogeneous, graphs can be structured into multiple layers to model context-specific interdependencies and more effectively capture the complexity of these interactions. This survey introduces a novel and comprehensive taxonomy that categorizes the diverse spectrum of multi-layer graph embedding methods into three main groups: algorithmic, machine learning, and deep learning approaches. This survey aims to serve as a guide for the research community in navigating the graph embedding methods for multi-layer graphs by providing a structured summary, analysis, and comparison within and across different categories that highlight their respective strengths, limitations, and suitability for various application domains. Furthermore, we examine key factors that influence the selection of appropriate methods, including graph structure, inherent properties, application domain, learning paradigm, and computational constraints. Finally, we outline several promising research directions to advance this rapidly evolving field.