Robutness of Scale-free Networks s fcfc 1 Random failure f c =1 ( 3) Attack = progressive failure of the most connected nodes f c <1 Internet maps R. Albert,

Presentación del tema: "Robutness of Scale-free Networks s fcfc 1 Random failure f c =1 ( 3) Attack = progressive failure of the most connected nodes f c <1 Internet maps R. Albert,"— Transcripción de la presentación:

1 Robutness of Scale-free Networks s fcfc 1 Random failure f c =1 ( 3) Attack = progressive failure of the most connected nodes f c <1 Internet maps R. Albert, H. Jeong, A.L. Barabasi, Nature 406 378 (2000)

2 Natural computer virus DNS-cache computer viruses Routing tables corruption Data carried viruses ftp, file exchange, etc. Internet topology Computer worms e-mail diffusing self-replicating E-mail network topology Ebel et al. (2002) Epidemic sprea on SF networks Epidemic spreading on SF networks EpidemiologyAir travel topology

3 Mathematical models of epidemics Coarse grained description of individuals and their state Individuals exist only in few states: Healthy or Susceptible * Infected * Immune * Dead Particulars on the infection mechanism on each individual are neglected. Topology of the system: the pattern of contacts along which infections spread in population is identified by a network Each node represents an individual Each link is a connection along which the virus can spread

4 Non-equilibrium phase transition epidemic threshold = critical point prevalence =order parameter c Active phase Absorbing phase Finite prevalence Virus death The epidemic threshold is a general result The question of thresholds in epidemics is central (in particular for immunization strategies) Each node is infected with rate if connected to one or more infected nodes Infected nodes are recovered (cured) with rate without loss of generality =1 (sets the time scale) Definition of an effective spreading rate = =prevalence SIS model:

5 What about computer viruses? Very long average lifetime (years!) compared to the time scale of the antivirus Small prevalence in the endemic case c Active phase Absorbing phase Finite prevalence Virus death Computer viruses ??? Long lifetime + low prevalence = computer viruses always tuned infinitesimally close to the epidemic threshold ???

6 SIS model on SF networks SIS= Susceptible – Infected – Susceptible Mean-Field usual approximation: all nodes are equivalent (same connectivity) => existence of an epidemic threshold 1/ for the order parameter density of infected nodes) Scale-free structure => necessary to take into account the strong heterogeneity of connectivities => k =density of infected nodes of connectivity k c = =>epidemic threshold

7 Order parameter behavior in an infinite system c = c 0 Epidemic threshold in scale-free networks

8 Rationalization of computer virus data Wide range of spreading rate with low prevalence (no tuning) Lack of healthy phase = standard immunization cannot drive the system below threshold!!!

9 If 3 we have absence of an epidemic threshold and no critical behavior. If 4 an epidemic threshold appears, but it is approached with vanishing slope (no criticality). If 4 the usual MF behavior is recovered. SF networks are equal to random graph. If 3 we have absence of an epidemic threshold and no critical behavior. If 4 an epidemic threshold appears, but it is approached with vanishing slope (no criticality). If 4 the usual MF behavior is recovered. SF networks are equal to random graph. Results can be generalized to generic scale-free connectivity distributions P(k)~ k -

10 Redes booleanas aleatorias inspiradas en las redes genéticas. Gen activo: produce proteína. Gen inhibido: no produce proteína. La presencia o ausencia de ciertas proteínas regulan la activación o inhibición de ciertos genes. Red genética: conjuntos de genes auto-regulados.

11 Modelo fago Lambda: ¿Qué podemos hacer cuando tenemos miles de genes acoplados? Stuart Kauffman 70s Redes booleanas aleatorias Random Boolean Networks (RBN)

12 K genes input PARÁMETROS: N = nº autómatas ~ genes K = conectividad Autónomo Síncrono Quenched Donde f es una función booleana de K argumentos booleanos. gen (autómata) 2 posibles valores: 0 = inactivo 1 = activo Ej. de RBN con K=3 y N=10 Definición de RBN

13

14 Ejemplo con N=13 y K=3

15 Cuenca anterior Las 2 13 = 8192 configuraciones globales en 15 cuencas de atracción disjuntas del ejemplo de RBN anterior.

16 Transición orden-desorden Orden (K=1) Desorden (K=3) K c =2

17 Enfoque ingeniero versus enfoque matemático N T ~ N g

18 Enfoque ingeniero versus enfoque matemático Propiedades generales: emergencia y evolución.

19 Generalización de las RBN Ricard V. Solé and Bartolo Luque. Phase transitions and antichaos in generalized Kauffman networks. Physics Letters A 196 (1995) pp. 331-334. Núcleo estable Método de la distancia (teoría de perturbaciones) Maxent (métodos variacionales)