Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and
applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda …

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing
users and items into latent representation space, with their correlative patterns from …

Hypergraphs extend traditional graphs by allowing edges to connect multiple nodes, while
superhypergraphs further generalize this concept to represent even more complex …

The second volume of “Advancing Uncertain Combinatorics through Graphization,
Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond” …

Hypergraph, an expressive structure with flexibility to model the higher-order correlations
among entities, has recently attracted increasing attention from various research domains …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Graph theory is afundamental branch of mathematicsthat studiesnetworksconsisting of
nodes (vertices) and their connections (edges). Extensive research has been conducted on …

Predicting a patient's future health condition by analyzing their Electronic Health Records
(EHRs) is a trending subject in the intelligent medical field, which can help clinicians …

Abstract GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular
metabolism and physiological states in living organisms. However, due to our imperfect …