HEAL: Unlocking the Potential of Learning on Hypergraphs Enriched With Attributes and Layers

The paper aims to explore the untapped potential of hypergraphs by leveraging attribute-rich and multi-layered structures. The primary objective is to develop an innovative learning framework, Hypergraph Learning Enriched with Attributes and Layers (HEAL), capable of effectively harnessing the complex relationships and information present in such data representations. Hypergraphs offer a more expressive and versatile way to model intricate relationships in real-world systems, accommodating entities with multiple interactions and diverse attributes. However, existing learning methods often overlook these unique features, especially cross-layer interactions, hindering their full potential. The motivation behind this research is to bridge this gap by creating HEAL, a novel learning approach that capitalises on attribute-rich and multi-layered hypergraphs to achieve superior performance across various applications. HEAL adopts a feature smoothing strategy to propagate attributes over the hypergraph structure, enabling the decoupling of feature propagation and transformation steps. This innovative methodology allows HEAL to capture the intricacies of multi-layer interactions while efficiently handling attribute-rich data. Moreover, the paper presents a detailed analysis of HEAL’s design and performance, showcasing its effectiveness in handling complex real-world datasets. The implications of HEAL are far-reaching and promising. By unlocking the potential of learning on hypergraphs enriched with attributes and layers, our work opens up new possibilities in various domains. This research contributes to the advancement of graph-based learning methods, paving the way for more sophisticated and efficient approaches in real-world applications.