7 papers
A General Solution for Network Models with Pairwise Edge Coupling
Alessio Catanzaro, Subodh Patil, Diego Garlaschelli
Network Models with couplings between link pairs are the simplest models for a class of networks with Higher Order interactions. In this paper we give an analytic, general solution…
Linking Through Time: Memory-Enhanced Community Discovery in Temporal Networks
Giulio Virginio Clemente, Diego Garlaschelli
Temporal Networks, and more specifically, Markovian Temporal Networks, present a unique challenge regarding the community discovery task. The inherent dynamism of these systems req…
Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networks
Maria Mircea, Diego Garlaschelli, Stefan Semrau
One of the main goals of developmental biology is to reveal the gene regulatory networks (GRNs) underlying the robust differentiation of multipotent progenitors into precisely spec…
Temporal networks with node-specific memory: unbiased inference of transition probabilities, relaxation times and structural breaks
Giulio Virginio Clemente, Claudio J. Tessone, Diego Garlaschelli
One of the main challenges in the study of time-varying networks is the interplay of memory effects with structural heterogeneity. In particular, different nodes and dyads can have…
Introduction to correlation networks: Interdisciplinary approaches beyond thresholding
Naoki Masuda, Zachary M. Boyd, Diego Garlaschelli +1
Many empirical networks originate from correlational data, arising in domains as diverse as psychology, neuroscience, genomics, microbiology, finance, and climate science. Speciali…
On nonlinear compression costs: when Shannon meets Rényi
Andrea Somazzi, Paolo Ferragina, Diego Garlaschelli
Shannon entropy is the shortest average codeword length a lossless compressor can achieve by encoding i.i.d. symbols. However, there are cases in which the objective is to minimize…