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The statistical physics of real-world networks
G. Cimini, T. Squartini, F. Saracco, D. Garlaschelli, A. Gabrielli, G.
Nature Reviews Physics
21 views
#networks #informationtheory #complexity
topic-5 (Extreme materials)
How much can we influence the rate of innovation?
T. Fink, M. Reeves
Science Advances
34 views
#innovation #strategy #management
topic-4 (Extreme materials)
The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market
F. Lillo, P. Barucca
Computational Management Science
334 views
#finance #banking #risk
topic-6 (Extreme materials)
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
94 views
#complexnetworks #energy #infrastructures
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
345 views
#randomgraphs #clustering #statisticalphysics
topic-5 (Extreme materials)
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
229 views
#technology #innovation #economics
topic-4 (Extreme materials)
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
172 views
#growth #complexsystems #cloud
topic-2 (Extreme materials)
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
162 views
#ecology #finance #entropy
topic-5 (Extreme materials) topic-6 (Low-energy computing)
Maximum one-shot dissipated work from Rényi divergences
N. Halpern, A. Garner, O. Dahlsten, V. Vedral
Physical Review E
55 views
#entropy #informationtheory #statisticalphysics
topic-3 (Extreme materials)
Eigenvalues of subgraphs of the cube
B. Bollobás, J. Lee, S. Letzter
European Journal of Combinatorics
162 views
#graph #combinatorics #asymptoticbehaviour
topic-5 (Extreme materials)
Tackling information asymmetry in networks: a new entropy-based ranking index
P. Barucca, G. Caldarelli, T. Squartini
Journal of Statistical Physics
51 views
#complexnetworks #Shannonentropy #informationtheory
topic-5 (Extreme materials)
Reconstructing grain-shape statistics from electron back-scatter diffraction microscopy
R. Farr, Z. Vukmanovic, M. Holness, E. Griffiths
Physical Review Materials
206 views
#EBSD #ElectronMicroscopy #Materials
topic-1 (Extreme materials)
On defining the Hamiltonian beyond quantum theory
D. Branford, O. Dahlsten, A. Garner
Foundations of Physics
47 views
#Hamiltonian #probabilistictheories #energy
topic-3 (Extreme materials)
119 papers
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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.