Spectral methods offer a tractable, global framework for clustering in graphs via eigenvector
computations on graph matrices. Hypergraph data, in which entities interact on edges of …

3D object classification is an important task in computer vision. In order to explore the high-
order and multi-modal correlations among 3D data, we propose an adaptive multi …

Message passing on hypergraphs has been a standard framework for learning higher-order
correlations between hypernodes. Recently-proposed hypergraph neural networks …

The aim of this paper is to revisit the definition of differential operators on hypergraphs,
which are a natural extension of graphs in systems based on interactions beyond pairs. In …

Many real-world systems involving higher-order interactions can be modeled by
hypergraphs, where vertices represent the systemic units and hyperedges describe the …

Identifying influential nodes in complex networks is one of the most important and
challenging problems to help optimize the network structure, control the spread of the …

Learning on hypergraphs has garnered significant attention recently due to their ability to
effectively represent complex higher-order interactions among multiple entities compared to …

We have established a coherent framework for applying variational methods to partial
differential equations on hypergraphs, which includes the propositions of calculus and …

This thesis generalizes the differential operators on standard oriented graphs and oriented
hypergraphs introduced in 10.1137/15M1022793 and arxiv: 2007.00325. The extended …

Higher-order information is crucial for relational learning in many domains where
relationships extend beyond pairwise interactions. Hypergraphs provide a natural …