Analytical maximum-likelihood method to detect patterns in real networks

DOI10.1088/1367-2630/13/8/083001zbMath1448.91218OpenAlexW3102635506MaRDI QIDQ5135892

Diego Garlaschelli, Tiziano Squartini

Publication date: 24 November 2020

Published in: New Journal of Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1367-2630/13/8/083001

Mathematics Subject Classification ID

Programming involving graphs or networks (90C35) Social networks; opinion dynamics (91D30)

Related Items (15)

Randomized Reference Models for Temporal Networks ⋮ Reconstruction methods for networks: the case of economic and financial systems ⋮ Reconstructing production networks using machine learning ⋮ Ensemble nonequivalence in random graphs with modular structure ⋮ Multilayer overlaps and correlations in the bank-firm credit network of Spain ⋮ Systemic risk of portfolio diversification ⋮ Fast analytical methods for finding significant labeled graph motifs ⋮ Sparse maximum-entropy random graphs with a given power-law degree distribution ⋮ Reconstructing the topology of financial networks from degree distributions and reciprocity ⋮ The multiplex structure of interbank networks ⋮ Reconstructing and stress testing credit networks ⋮ Binary versus non-binary information in real time series: empirical results and maximum-entropy matrix models ⋮ A GDP-driven model for the binary and weighted structure of the International Trade Network ⋮ Unbiased sampling of network ensembles ⋮ Large deviation and anomalous fluctuations scaling in degree assortativity on configuration networks

Cites Work

• Connected components in random graphs with given expected degree sequences
• Statistical mechanics of complex networks
• Information Theory and Statistical Mechanics
• Scale-Free Networks
• The Structure and Function of Complex Networks

This page was built for publication: Analytical maximum-likelihood method to detect patterns in real networks