Community partition is an important problem in many areas, such as biology networks and
social networks. The objective of this problem is to analyze the relationships among data via …

Robust statistics is a fairly mature field that dates back to the early 1960s, with many
foundational concepts having been developed in the ensuing decades. However, the field …

In this work, we study the problem of community detection in the stochastic block model with
adversarial node corruptions. Our main result is an efficient algorithm that can tolerate an ϵ …

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 propose a new, efficiently solvable hierarchy of semidefinite programming relaxations for
inference problems. As test cases, we consider the problem of community detection in block …

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 …

In the study of sparse stochastic block models (SBMs) one often needs to analyze a
distributional recursion, known as the belief propagation (BP) recursion. Uniqueness of the …

Community detection in random graphs or hypergraphs is an interesting fundamental
problem in statistics, machine learning and computer vision. When the hypergraphs are …

We study robust community detection in the context of node-corrupted stochastic block
model, where an adversary can arbitrarily modify all the edges incident to a fraction of the n …

Spectral algorithms, such as principal component analysis and spectral clustering, rely on
the extremal eigenpairs of a matrix A. However, these may be uninformative without …