Export:
list
,
tags
,
impacts
Memristive networks: from graph theory to statistical physics
A. Zegarac, F. Caravelli
Europhysics Letters
18 views
#graph #statistics #memristive
The statistical physics of real-world networks
G. Cimini, T. Squartini, F. Saracco, D. Garlaschelli, A. Gabrielli, G.
Nature Reviews Physics
37 views
#networks #informationtheory #complexity
topic-5 (Graph theory)
How much can we influence the rate of innovation?
T. Fink, M. Reeves
Science Advances
62 views
#innovation #strategy #management
topic-4 (Science of innovation)
Exactly solvable random graph ensemble with extensively many short cycles
F. Lopez, P. Barucca, M. Fekom, A. Coolen
Journal of Physics A: Mathematical and Theoretical
358 views
#randomgraphs #clustering #statisticalphysics
topic-5 (Graph theory)
The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market
F. Lillo, P. Barucca
Computational Management Science
341 views
#finance #banking #risk
topic-6 (Mathematics of risk)
A complex network approach for the estimation of the energy demand of electric mobility
M. Mureddu, A. Facchini, A. Scala, G. Caldarelli, A. Damiano
Scientific Reports
99 views
#complexnetworks #energy #infrastructures
How well do experience curves predict technological progress? A method for making distributional forecasts
F. Lafond, A. Bailey, J. Bakker, D. Rebois, R. Zadourian, P. McSharry, D. Farmer
Technological Forecasting and Social Change
235 views
#technology #innovation #economics
topic-4 (Science of innovation)
A holistic approach for collaborative workload execution in volunteer clouds
S. Sebastio, M. Amoretti, A. Lafuente, A. Scala
ACM Transactions on Modeling and Computer Simulation
174 views
#growth #complexsystems #cloud
topic-2 (Low-energy computing)
From ecology to finance (and back?): a review on entropy-based null models for the analysis of bipartite networks
M. Straka, G. Caldarelli, T. Squartini, F. Saracco
Journal of Statistical Physics
167 views
#ecology #finance #entropy
topic-5 (Graph theory) topic-6 (Mathematics of risk)
Maximum one-shot dissipated work from Rényi divergences
N. Halpern, A. Garner, O. Dahlsten, V. Vedral
Physical Review E
56 views
#entropy #informationtheory #statisticalphysics
topic-3 (Quantum information)
Eigenvalues of subgraphs of the cube
B. Bollobás, J. Lee, S. Letzter
European Journal of Combinatorics
166 views
#graph #combinatorics #asymptoticbehaviour
topic-5 (Graph theory)
Tackling information asymmetry in networks: a new entropy-based ranking index
P. Barucca, G. Caldarelli, T. Squartini
Journal of Statistical Physics
53 views
#complexnetworks #Shannonentropy #informationtheory
topic-5 (Graph theory)
Reconstructing grain-shape statistics from electron back-scatter diffraction microscopy
R. Farr, Z. Vukmanovic, M. Holness, E. Griffiths
Physical Review Materials
210 views
#EBSD #ElectronMicroscopy #Materials
topic-1 (Extreme materials)
120 papers
Citation updates
Online media updates
The statistical physics of real-world networks
G. Cimini, T. Squartini, F. Saracco, D. Garlaschelli, A. Gabrielli, G.
Nature Reviews Physics
1, 58 (08/19)
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 phenomena, such as the emergence of mixed distributions and ensemble non-equivalence, that are observed in heterogeneous networks but not in homogeneous systems. At the same time, thanks to the deep connection between the principle of maximum entropy and information theory, statistical physics has led to the definition of null models for networks that reproduce features of real-world systems but that are otherwise as random as possible. We review here the statistical physics approach and the null models for complex networks, focusing in particular on analytical frameworks that reproduce local network features. We show how these models have been used to detect statistically significant structural patterns in real-world networks and to reconstruct the network structure in cases of incomplete information. We further survey the statistical physics models that reproduce more complex, semilocal network features using Markov chain Monte Carlo sampling, as well as models of generalized network structures, such as multiplex networks, interacting networks and simplicial complexes.