In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …

High-order clustering aims to identify heterogeneous substructures in multiway datasets that
arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex …

Conventional network data have largely focused on pairwise interactions between two
entities, yet multi-way interactions among multiple entities have been frequently observed in …

Spectral clustering is a celebrated algorithm that partitions the objects based on pairwise
similarity information. While this approach has been successfully applied to a variety of …

Community detection in network data aims at grou** similar nodes sharing certain
characteristics together. Most existing methods focus on detecting communities in …

Community detection in complex networks is widely used across fields, from sociology to
biology. Despite its broad applications, there has been limited exploration of real-world …

We consider the community detection problem in a sparse-uniform hypergraph, assuming
that is generated according to the Hypergraph Stochastic Block Model (HSBM). We prove …

We study the problem of community detection in a random hypergraph model which we call
the stochastic block model for $ k $-uniform hypergraphs ($ k $-SBM). We investigate the …

This paper investigates fundamental limits of exact recovery in the general-uniform
hypergraph stochastic block model (-HSBM), wherein nodes are partitioned into disjoint …

We consider the community detection problem in sparse random hypergraphs. Angelini et
al. in [6] conjectured the existence of a sharp threshold on model parameters for community …