activity
20062008
most citedMaximum likelihood: extracting unbiased information from complex networks

195 citations · 711 across the 6 of their papers we have counts for

collaborators

6 papers

physics.soc-ph2008146 cited

Generalized Bose-Fermi statistics and structural correlations in weighted networks

Diego Garlaschelli, Maria I. Loffredo

We derive a class of generalized statistics, unifying the Bose and Fermi ones, that describe any system where the first-occupation energies or probabilities are different from subs…

physics.soc-ph200889 cited

On the rich-club effect in dense and weighted networks

Vinko Zlatic, Ginestra Bianconi, Albert Diaz-Guilera +3

For many complex networks present in nature only a single instance, usually of large size, is available. Any measurement made on this single instance cannot be repeated on differen…

q-fin.GN200740 cited

Effects of network topology on wealth distributions

Diego Garlaschelli, Maria I. Loffredo

We focus on the problem of how wealth is distributed among the units of a networked economic system. We first review the empirical results documenting that in many economies the we…

physics.soc-ph2007133 cited

Interplay between topology and dynamics in the World Trade Web

D. Garlaschelli, T. Di Matteo, T. Aste +2

We present an empirical analysis of the network formed by the trade relationships between all world countries, or World Trade Web (WTW). Each (directed) link is weighted by the amo…

cond-mat.stat-mech2006108 cited

Self-organized network evolution coupled to extremal dynamics

Diego Garlaschelli, Andrea Capocci, Guido Caldarelli

The interplay between topology and dynamics in complex networks is a fundamental but widely unexplored problem. Here, we study this phenomenon on a prototype model in which the net…

cond-mat.dis-nn2006195 cited

Maximum likelihood: extracting unbiased information from complex networks

Diego Garlaschelli, Maria I. Loffredo

The choice of free parameters in network models is subjective, since it depends on what topological properties are being monitored. However, we show that the Maximum Likelihood (ML…