In the past 15 years, statistical physics has been successful as a framework for modelling
complex networks. On the theoretical side, this approach has unveiled a variety of physical …

Closed-form, interpretable mathematical models have been instrumental for advancing our
understanding of the world; with the data revolution, we may now be in a position to uncover …

Represented as graphs, real networks are intricate combinations of order and disorder.
Fixing some of the structural properties of network models to their values observed in real …

Network models with latent geometry have been used successfully in many applications in
network science and other disciplines, yet it is usually impossible to tell if a given real …

Abstract Exponential-family Random Graph Models (ERGMs) have long been at the forefront
of the analysis of relational data. The exponential-family form allows complex network …

Understanding the rat neurochemical connectome is fundamental for exploring neuronal
information processing. By using advanced data mining, supervised machine learning, and …

Existing information-theoretic frameworks based on maximum entropy network ensembles
are not able to explain the emergence of heterogeneity in complex networks. Here, we fill …

Exponential random graph models have attracted significant research attention over the past
decades. These models are maximum-entropy ensembles subject to the constraints that the …

Stochastic processes under resetting at random times have attracted a lot of attention in
recent years and served as illustrations of nontrivial and interesting static and dynamic …

We investigate the procedure of semi-parametric maximum likelihood estimation under
constraints on summary statistics. Such a procedure results in a discrete probability …