Going beyond networks, to include higher-order interactions of arbitrary sizes, is a major
step to better describe complex systems. In the resulting hypergraph representation, tools to …

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

Hypergraphs are a powerful abstraction for modeling high-order relations, which are
ubiquitous in many fields. A hypergraph consists of nodes and hyperedges (ie, subsets of …

We study ensembles of sparse block-structured random matrices generated from the
adjacency matrix of a Erdös–Renyi random graph with N vertices of average degree Z …

Abstract Modern Deep Neural Networks (DNNs) have achieved very high performance at
the expense of computational resources. To decrease the computational burden, several …

The problem of finding the degeneracy of a graph is a subproblem of the k-core
decomposition problem. In this paper, we present a (1+ epsilon)-approximate solution to the …

In this document, we discuss future applications and directions of hypergraph mining,
especially hypergraph patterns. We mainly review and discuss existing applications and …

Recent advances in IoT and AI technologies have enabled mobile IoT devices to provide
live video services. In these services, attempts to apply data learning to network control are …

The problem of finding the degeneracy of a graph is a subproblem of the k-core
decomposition problem. In this paper, we present a (1+ ϵ)-approximate solution to the …

As the technological evolution of machine learning is accelerating nowadays, data plays a
vital role in building intelligent models, being able to simulate phenomena, predict values …