Transcript Document

Generalized Percolation
in Complex Networks
Shlomo Havlin

Reuven Cohen
Tomer Kalisky
Alex Rozenfeld
Eugene Stanley
Lidia Braunstein
Sameet Sreenivasan
Gerry Paul

Daniel ben- Avraham
Bar-Ilan University
Boston Universtiy
Clarkson University
Outline
 Percolation – Graph Theory: Introduction
 Complex Networks: Theory vs Experiment
 Degree Distribution, Critical Concentration, Distance
 Generalized Networks: Broad Degree Distribution –
Anomalous physics
Applications:
 Efficient Immunization Strategy
 Optimal path - Optimal transport
 Optimize Network Stability
References
Cohen et al
Rozenfeld et al
Cohen and Havlin
Braunstein et al
Cohen et al
Paul et al
Phys. Rev. Lett. 85, 4626 (2000); 86, 3682 (2001)
Phys. Rev. Lett. 89, 218701 (2002)
Phys. Rev. Lett. 90, 58705 (2003)
Phys. Rev. Lett. 91,247901 (2003)
Phys. Rev. Lett. 91,247901 (2003)
Europhys. J. B (in press) (cond-mat/0404331)
Percolation and Immunization
Percolation theory
p  0.3
remove
or
immune
Network exists
P
Prob. that a site belongs
to a spanning cluster
1
P ~ p  pc 

p  0.5
remove
or
immune
0
Network collapse
pc
p
Random Graph Theory
 Developed in the 1960’s by Erdos and Renyi. (Publications of the Mathematical
Institute of the Hungarian Academy of Sciences, 1960).
 Discusses the ensemble of graphs with N vertices and M edges (2M links).
 Distribution of connectivity per vertex is Poissonian (exponential),
where k is the number of links :
P( k )  e c
ck
k! ,
c k 
 Distance d=log N
--
2M
N
SMALL WORLD
Known Results
 Phase transition at average connectivity, k  1 :
k  1 No spanning cluster (giant component) of order logN
k  1 A spanning cluster exists (unique) of order N
k  1 The largest cluster is of order N 2 / 3
 Size of the spanning cluster is determined by the self-consistent equation:
P  1  e  k
P
 Behavior of the spanning cluster size near the transition is linear:
P  ( pc  p )  ,   1 , where p is the probability of deleting a site,
pc  1  1/ k
Percolation on a Cayley Tree
 Contains no loops
 Connectivity of each node is fixed ( z connections)
 Critical threshold:
1
pc 
z 1
 Behavior of the spanning cluster size near the transition is linear:
P  ( pc  p )  ,   1
In Real World - Many Networks are non-Poissonian
k
P( k )  e
 k
k
k!
ck  
P(k )  
 0
mk K
otherwise
Internet Network
Networks in Physics
Erdös Theory is Not Valid
Stability and Immunization
pc  1 
1
k 1
Distance
Distribution
d ~ log N
Critical concentration 30-50%
Critical
Infectious disease concentration
Malaria
99%
Measles
90-95%
Whooping cough 90-95%
Fifths disease
90-95%
Chicken pox
85-90%
Mumps
85-90%
Rubella
82-87%
Poliomyelitis
82-87%
Diphtheria
82-87%
Scarlet fever
82-87%
Smallpox
70-80%
INTERNET
99%
Generalization of Erdös Theory:
Cohen, Erez, ben-Avraham, Havlin, PRL 85, 4626 (2000)
Epidemiology Theory: Vespignani, Pastor-Satoral,
PRL (2001), PRE (2001)
Almost constant
(Metabolic Networks,
Jeong et. al.
(Nature, 2000))
Cohen, Havlin,
Phys. Rev. Lett. 90, 58701(2003)
Modelling: Albert, Jeong, Barabasi (Nature 2000)
Distance in Scale Free Networks
P(k ) ~ k
d  const.
Ultra
Small
World
Small World

2
d  log log N
2 3
log N
d
log log N
d  log N
 3
3
(Bollobas, Riordan, 2002)
(Bollobas, 1985)
(Newman, 2001)
Cohen, Havlin Phys. Rev. Lett. 90, 58701(2003)
Cohen, Havlin and ben-Avraham, in Handbook of Graphs and Networks
Eds. Bornholdt and Shuster (Willy-VCH, NY, 2002) Chap. 4
Confirmed also by: Dorogovtsev et al (2002), Chung and Lu (2002)
Critical Threshold
Scale Free
General result:
pc  1 
robust
K0 
Poor immunization
1
K0 1
k2
k
For Poisson:
pc
Acquaintance
vulnerable
Intentional
K0 
k2
k
pc  1 
Efficient immunization

k
2
 k
k
1
k 1
Efficient Immunization
Strategies:
Cohen et al Phys. Rev. Lett. 91 , 168701 (2003)
Acquaintance Immunization
Not only critical thresholds but also critical exponents are different !
THE UNIVERSALITY CLASS DEPENDS ON THE WAY CRITICALITY REACHED
Critical Exponents
Using the properties of power series (generating functions) near a singular point
(Abelian methods), the behavior near the critical point can be studied.
(Diff. Eq. Molloy & Reed (1998) Gen. Func. Newman Callaway PRL(2000), PRE(2001))
For random breakdown the behavior near criticality in scale-free networks is different than for
random graphs or from mean field percolation. For intentional attack-same as mean-field.
Even for networks with 3    4, where k and k are finite, the critical exponents change
from the known mean-field result   1. The order of the phase transition and the exponents
are determined by k 3 .
2
Size of the infinite cluster:
P ~ ( p  pc ) 
 1
3  

 1
 
  3

 1

2 3
3  4
4
(known mean field)
Distribution of finite clusters at criticality:
ns ~ s

 2  3


    2
 2.5


4
4
(known mean field)
Optimal Distance - Disorder
.
2
D
.
E
1
4
.
A
Path from
A to B
.
C
5
3
wi = weight = price, quality,
w
i
i
lmin= 2(ACB)
lopt= 3(ADEB)
.
B
time…..
 minimal  optimal path
Weak disorder (WD) – all wi contribute to the sum (narrow distribution)
Strong disorder (SD)– a single term dominates the sum (broad distribution)
SD – example: Broadcasting video over the Internet.
A transmission at constant high rate is needed.
The narrowest band width link in the path
between transmitter and receiver controls the rate.
Scale Free (Barabasi-Albert)
Random Graph (Erdos-Renyi)
Small World (Watts-Strogatz)
Z=4
Optimal path – strong disorder
Random Graphs and Watts Strogatz Networks
CONSTANT SLOPE
N – total number of nodes
lopt ~ N
1
3
Analytically and Numerically
LARGE WORLD!!
Mapping to shortest path at critical percolation
Compared to the diameter or
average shortest path
lmin ~ log N
(small world)
n0 - typical range of neighborhood
without long range links
N
- typical number of nodes with
n0
long range links
Scale Free +ER – Optimal Path
Strong Disorder
Theoretically
+
Numerically
N (  3) /( 1)

 1
lopt ~ N 3 log N
 13

N
3  4
4
LARGE WORLD!!
Numerically
lopt ~ log 1 N
Diameter – shortest path
  4  ER
lmin
2 3
3
log N

~ log N / log log N
3
 log log N
23

SMALL WORLD!!
Weak Disorder
lopt ~ log N for all 
For 2    3 lopt ~ exp(lmin )
Braunstein et al Phys. Rev. Lett. 91, 247901 (2003)
Optimal Networks
Simultaneous waves of targeted and random attacks
pt - Fraction of targeted
Bimodal: fraction of (1-r) having k 1 links
and r having k2  ( k  1  r ) / r links
r = 0.001—0.15 from left to right
Optimal Bimodal :
r  2( pt / pr )
pr - Fraction of random
 k2 
Condition for connectivity:  
2
k 
P(k) changes:
k 
P(k )   P0 (k ) 0 (1  pr ) k prk0 k
k0
k
K
Bimodal
Bi-modal
P(k) changes also due to targeted attack
For pt ,
fc
SF
SF
r=0.001
0.01
pr  1, pt / pr
is the only parameter
- critical threshold
Paul et al. Europhys. J. B 38, 187
(2004), (cond-mat/0404331)
Tanizawa et al. Optimization of
Network Robustness to Waves of
Targeted and Random Attacks
(Cond-mat/0406567)
Optimal Bimodal
Specific example:
Given: N=100, 
and
k  2.1 , k1  1
pt / pr  0.05  r  0.1
i.e, 10 “hubs” of degree
using
k 2  12
k2  ( k  1  r ) / r
Conclusions and Applications
•Generalized percolation P(k)~k-λ , >4 – Erdos-Renyi, <4 – novel topology –novel
physics.
•Distance in scale free networks <3 : d~loglogN - ultra small world, >3 : d~logN.
•Optimal distance – strong disorder – ER, WS and SF (>4)
{
lopt ~ N
scale free
 3
 1
lopt ~ log  1 N
lopt ~ N
for   3
for
1
3
Large World
 Large World
2    3  Small World
• Scale Free networks (2<λ<3) are robust to random breakdown.
• Scale Free networks are vulnerable to targeted attack on the highly connected nodes.
• Efficient immunization is possible without knowledge of topology, using Acquaintance
Immunization.
• The critical exponents for scale-free directed and non-directed networks are different than
those in exponential networks – different universality class!
•Large networks can have their connectivity distribution optimized for maximum robustness
to random breakdown and/or intentional attack.