activity
20102026
most citedFrom random matrices to long range dependence

14 citations · 57 across the 43 of their papers we have counts for

collaborators

63 papers

math.PR2026

The Generalized Friendship Paradox for Eigenvectors

Bishakh Bhattacharya, Arijit Chakrabarty, Rajat Subhra Hazra

In this paper, we investigate the generalized friendship paradox for eigenvectors (alternatively called the eigen friendship paradox and abbreviated hereafter as EFP) in the settin…

math.PR2026

Renormalisation of Inhomogeneous Random Graphs

Luca Avena, Diego Garlaschelli, Rajat Subhra Hazra +1

We consider inhomogeneous random graphs in which vertices are assigned i.i.d.\ random weights, pairs of distinct vertices are connected by an edge independently with a probability…

math.PR2026

Spectrum of Directed Inhomogeneous Random Graphs

Rajat Subhra Hazra, Giacomo Passuello

We study the spectrum of the adjacency matrix of directed inhomogeneous random graphs on vertices. We assume that has independent entries and diverging average degr…

math.PR2026

Outlier eigenvalues and eigenvectors of generalized Wigner matrices with finite-rank perturbations

Bishakh Bhattacharya, Arijit Chakrabarty, Rajat Subhra Hazra

A generalized Wigner matrix perturbed by a finite-rank deterministic matrix is considered. The fluctuations of the largest eigenvalues, which emerge outside the bulk of the spectru…

math.PR2025

Functional central limit theorem for subgraph counts in a dynamic random connection model

Rajat Subhra Hazra, Nikolai Kriukov, Michel Mandjes +1

We prove a functional central limit theorem for subgraph counts in a dynamic version of the random connection model. To establish tightness, we develop a dynamic extension of the c…

math.SP2025

Spectra of random graphs with discrete scale invariance

Alessio Catanzaro, Rajat Subhra Hazra, Diego Garlaschelli

Random graphs defined by an occurrence probability that is invariant under node aggregation have been identified recently in the context of network renormalization. The invariance…