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 …

Hypergraphs, which belong to the family of higher-order networks, are a natural and
powerful choice for modeling group interactions in the real world. For example, when …

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

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 …

Abstract Electro-Hydrostatic Actuator (EHA) is a critical electro-hydraulic actuator system
widely used in aerospace equipment. To ensure its normal operation, the intelligent fault …

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 …

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

This paper delves into the problem of correlated time-series forecasting in practical
applications, an area of growing interest in a multitude of fields such as stock price …