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 …

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” …

As real-world data become increasingly heterogeneous, multi-view semi-supervised
learning has garnered widespread attention. Although existing studies have made efforts …

As many readers may know, graph theory is a fundamental branch of mathematics that
explores networks made up of nodes and edges, focusing on their paths, structures, and …

This paper investigates the properties of tree-width and related graph width parameters for n-
SuperHyperGraphs, a broader generalization of hypergraphs. By exploring concepts such …

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 …

Providing potential next point-of-interest (POI) suggestions for users has become a
prominent task in location-based social networks, which receives more and more attention …

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to
understand and design deep learning models. This paper posits that TDL is the new frontier …