I am a research scientist at the Istituto dei Sistemi Complessi (Institute of Complex Systems, ISC-CNR), part of CNR, the National Research Council of Italy. ## Some recent papers- We consider cumulative merging percolation (CMP), a long-range percolation process describing the iterative merging of clusters in networks, depending on their mass and mutual distance. For a specific class of CMP processes, which represents a generalization of degree-ordered percolation, we derive a scaling solution on uncorrelated complex networks, unveiling the existence of diverse mechanisms leading to the formation of a percolating cluster. The scaling solution accurately reproduces universal properties of the transition. This finding is used to infer the critical properties of the susceptible-infected-susceptible model for epidemics in infinite and finite power-law distributed networks. Here, discrepancies between analytical approaches and numerical results regarding the finite-size scaling of the epidemic threshold are a crucial open issue in the literature. We find that the scaling exponent assumes a nontrivial value during a long preasymptotic regime. We calculate this value, finding good agreement with numerical evidence. We also show that the crossover to the true asymptotic regime occurs for sizes much beyond currently feasible simulations. Our findings allow us to rationalize and reconcile all previously published results (both analytical and numerical), thus ending a long-standing debate.
- Among the consequences of the disordered interaction topology underlying
many social, technological, and biological systems, a particularly
important one is that some nodes, just because of their position in the
network, may have a disproportionate effect on dynamical processes
mediated by the complex interaction pattern. For example, the early
adoption of a commercial product by an opinion leader in a social
network may change its fate or just a few superspreaders may determine
the virality of a meme in social media. Despite many recent efforts, the
formulation of an accurate method to optimally identify influential
nodes in complex network topologies remains an unsolved challenge. Here,
we present the exact solution of the problem for the specific, but
highly relevant, case of the susceptible-infected-removed (SIR) model
for epidemic spreading at criticality. By exploiting the mapping between
bond percolation and the static properties of the SIR model, we prove
that the recently introduced nonbacktracking centrality is the optimal
criterion for the identification of influential spreaders in locally
tree-like networks at criticality. By means of simulations on synthetic
networks and on a very extensive set of real-world networks, we show
that the nonbacktracking centrality is a highly reliable metric to
identify top influential spreaders also in generic graphs not embedded
in space and for noncritical spreading.
- Among the consequences of the disordered interaction topology underlying
many social, technological, and biological systems, a particularly
important one is that some nodes, just because of their position in the
network, may have a disproportionate effect on dynamical processes
mediated by the complex interaction pattern. For example, the early
adoption of a commercial product by an opinion leader in a social
network may change its fate or just a few superspreaders may determine
the virality of a meme in social media. Despite many recent efforts, the
formulation of an accurate method to optimally identify influential
nodes in complex network topologies remains an unsolved challenge. Here,
we present the exact solution of the problem for the specific, but
highly relevant, case of the susceptible-infected-removed (SIR) model
for epidemic spreading at criticality. By exploiting the mapping between
bond percolation and the static properties of the SIR model, we prove
that the recently introduced nonbacktracking centrality is the optimal
criterion for the identification of influential spreaders in locally
tree-like networks at criticality. By means of simulations on synthetic
networks and on a very extensive set of real-world networks, we show
that the nonbacktracking centrality is a highly reliable metric to
identify top influential spreaders also in generic graphs not embedded
in space and for noncritical spreading.
F. Radicchi and - Among the consequences of the disordered interaction topology underlying
many social, technological, and biological systems, a particularly
important one is that some nodes, just because of their position in the
network, may have a disproportionate effect on dynamical processes
mediated by the complex interaction pattern. For example, the early
adoption of a commercial product by an opinion leader in a social
network may change its fate or just a few superspreaders may determine
the virality of a meme in social media. Despite many recent efforts, the
formulation of an accurate method to optimally identify influential
nodes in complex network topologies remains an unsolved challenge. Here,
we present the exact solution of the problem for the specific, but
highly relevant, case of the susceptible-infected-removed (SIR) model
for epidemic spreading at criticality. By exploiting the mapping between
bond percolation and the static properties of the SIR model, we prove
that the recently introduced nonbacktracking centrality is the optimal
criterion for the identification of influential spreaders in locally
tree-like networks at criticality. By means of simulations on synthetic
networks and on a very extensive set of real-world networks, we show
that the nonbacktracking centrality is a highly reliable metric to
identify top influential spreaders also in generic graphs not embedded
in space and for noncritical spreading.
F. Radicchi and - The stochastic addition of either vertices or connections in a network
leads to the observation of the percolation transition, a structural
change with the appearance of a connected component encompassing a
finite fraction of the system. Percolation has always been regarded as a
substrate-dependent but model-independent process, in the sense that
the critical exponents of the transition are determined by the geometry
of the system, but they are identical for the bond and site percolation
models. Here, we report a violation of such assumption. We provide
analytical and numerical evidence of a difference in the values of the
critical exponents between the bond and site percolation models in
networks with null percolation thresholds, such as scale-free graphs
with diverging second moment of the degree distribution. We discuss
possible implications of our results in real networks, and provide
additional insights on the anomalous nature of the percolation
transition with null threshold. reported.spreading in
coevolving, coupled, and time-varying networks is reported.
F. Radicchi and C. CastellanoBreaking of the site-bond percolation universality in networks Nat. Commun. 6, 10196 (2015). In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.
R. Pastor-Satorras, C. Castellano, P. Van Mieghem and A. VespignaniEpidemic processes in complex networks Rev. Mod. Phys. 87, 925 (2015). [arxiv:1408.2701] - Human languages are rule governed, but almost invariably these rules have exceptions in the form of irregularities. Since rules in language are efficient and productive, the persistence of irregularity is an anomaly. How does irregularity linger in the face of internal (endogenous) and external (exogenous) pressures to conform to a rule? Here we address this problem by taking a detailed look at simple past tense verbs in the Corpus of Historical American English. The data show that the language is open, with many new verbs entering. At the same time, existing verbs might tend to regularize or irregularize as a consequence of internal dynamics, but overall, the amount of irregularity sustained by the language stays roughly constant over time. Despite continuous vocabulary growth, and presumably, an attendant increase in expressive power, there is no corresponding growth in irregularity. We analyze the set of irregulars, showing they may adhere to a set of minority rules, allowing for increased stability of irregularity over time. These findings contribute to the debate on how language systems become rule governed, and how and why they sustain exceptions to rules, providing insight into the interplay between the emergence and maintenance of rules and exceptions in language.
C. F. Cuskley, M. Pugliese, Internal and external dynamics in language: Evidence from verb regularity in a historical corpus of EnglishC. Castellano, F. Colaiori, V. Loreto and F. TriaPLoS ONE 9(8): e102882 (2014). [arxiv:1408.2699] - We develop an analytical approach to the susceptible-infected-susceptible (SIS) epidemic model that allows us to unravel the true origin of the absence of an epidemic threshold in heterogeneous networks. We find that a delicate balance between the number of high degree nodes in the network and the topological distance between them dictates the existence or absence of such a threshold. In particular, small-world random networks with a degree distribution decaying slower than an exponential have a vanishing epidemic threshold in the thermodynamic limit.
M. Boguñá, The nature of the epidemic threshold for the susceptible-infected-susceptible dynamics in networksC. Castellano and R. Pastor-SatorrasPhys. Rev. Lett. 111, 068701 [arxiv:1305.4819] |