Multi Aspect Graph

Multi-aspect graphs represent complex systems with multiple interconnected relationships between entities, aiming to model and analyze data with diverse perspectives or features. Current research focuses on developing robust algorithms, such as those incorporating optimal transport and tensor decomposition, to efficiently analyze these graphs, particularly in large-scale and dynamic settings. These methods are applied to diverse problems, including patient pathway analysis in healthcare, knowledge graph alignment, and job title normalization, demonstrating the broad applicability and impact of multi-aspect graph analysis across various domains.