5 citations · 7 across the 7 of their papers we have counts for
7 papers
Colorful Exponential Random Graph Models
Bhaswar B. Bhattacharya, Pierfrancesco Dionigi, Ankan Ganguly +1
In this paper, we initiate the study of colored exponential random graph models (ERGMs), a class of exponential-family models for networks with multiple types of edge relations. Us…
Preferential Attachment with Local Flexibility
Tingyu Zhao, Balázs Maga, Pierfrancesco Dionigi +6
From the formation of social ties to the budding quantum internet, growing networks often exhibit local flexibility upon new nodes attaching to an existing network. In our proposed…
Large deviations for probability graphons
Pierfrancesco Dionigi, Giulio Zucal
We establish a large deviation principle (LDP) for probability graphons, which are symmetric functions from the unit square into the space of probability measures. This notion exte…
The effects of initial conditions on the accuracy of mean-field approximations of Markov processes on large random graphs
Pierfrancesco Dionigi, Dániel Keliger
We study the evolution of a general class of stochastic processes (containing, e.g. SIS and SIR models) on large random networks, focusing on a particular general class of random g…
Largest Eigenvalue of the Configuration Model and Breaking of Ensemble Equivalence
Pierfrancesco Dionigi, Diego Garlaschelli, Rajat Subhra Hazra +1
We analyse the largest eigenvalue of the adjacency matrix of the configuration model with large degrees, where the latter are treated as hard constraints. In particular, we compute…
Central limit theorem for the principal eigenvalue and eigenvector of Chung-Lu random graphs
Pierfrancesco Dionigi, Diego Garlaschelli, Rajat Subhra Hazra +2
A Chung-Lu random graph is an inhomogeneous Erdős-Rényi random graph in which vertices are assigned average degrees, and pairs of vertices are connected by an edge with a probabili…
A spectral signature of breaking of ensemble equivalence for constrained random graphs
Pierfrancesco Dionigi, Diego Garlaschelli, Frank den Hollander +1
For random systems subject to a constraint, the microcanonical ensemble requires the constraint to be met by every realisation ("hard constraint"), while the canonical ensemble req…