Introduction to complex Introduction to complex networks networks Part II: Models Part II: Models Ginestra Bianconi Physics Department,Northeastern University, Boston,USA NetSci 2010 Boston, May 10 2010 QuickTime™ and a decompressor are needed to see this picture QuickTime™ and a decompressor are needed to see this picture.
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Introduction to complex networks Part II: Models Ginestra Bianconi Physics Department,Northeastern University, Boston,USA NetSci 2010 Boston, May 10 2010.
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Introduction to complex networksIntroduction to complex networksPart II: ModelsPart II: Models
Chung & Lu 2002, Caldarelli et al. 2002, Park & Newman 2003
Motivation for BA modelMotivation for BA model
1) The network growNetworks continuously expand by the addition of new nodesEx. WWW : addition of new documents Citation : publication of new papers2) The attachment is not uniform
(preferential attachment).
A node is linked with higher probability to a node that already has a large number of links.
Ex: WWW : new documents link to well known sites (CNN, YAHOO, NewYork Times, etc) Citation : well cited papers are more likely to be cited again
BA modelBA model(1) GROWTH : At every timestep we add a new node with m edges (connected to the nodes already present in the system).
(2) PREFERENTIAL ATTACHMENT : The probability Π that a new node will be connected to node i depends on the connectivity ki of that node
Barabási et al. Science (1999)
jj
ii k
kk
Σ=Π )(
P(k) ~k-3
Result of the BA scale-free Result of the BA scale-free modelmodel
The connectivity of each node increases in time as a power-law with exponent 1/2:
The probability that a node has k links follow a power-law with exponent γ:
€
ki(t) = mt
ti
€
P(k) = 2m2 1
k 3
Initial attractiveness
The initial attractiveness can change the value of the power-law exponent γ
€
Πi ∝ ki + A
€
γ∈ (2,∞)
β(γ −1) =1
€
ki ∝t
ti
⎛
⎝ ⎜
⎞
⎠ ⎟
β
P(k) ∝ k −γ
A preferential attachment with initial attractiveness A yields
Other variationsScale-free networks with high-clustering coefficient
Dorogovtsev et al. 2001
Eguiluz & Klemm 2002
Aging of the nodes
Dorogovstev & Medes 2000
Pseudofractal scale-free network
Dorogovtsev et al 2002
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Features of the nodesFeatures of the nodesIn complex networks
nodes are generally heterogeneous and they are characterized by specific features
Social networks: age, gender, type of jobs, drinking and smoking habits, Internet: position of routers in geographical space, … Ecological networks: Trophic levels, metabolic rate, philogenetic distance Protein interaction networks: localization of the protein inside the cell, protein
concentration
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Fitness the nodes Fitness the nodes
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Not all the nodes are the same!
Let assign to each node an
energy
of a node
In the limit =0 all the nodes have same fitness
TheThe fitness model fitness model
Growth:
–At each time a new node and m links are added to the network.
–To each node i we assign a energy i from a p() distribution
Generalized preferential attachment:–Each node connects to the rest of the network by m links attached preferentially to well connected, low energy nodes.
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6
€
Πi ∝ e−βε i ki
Results of the model
€
P(k) ≈ k −γ 2 < γ < 3
Power-law degree distribution
Fit-get-rich mechanismFit-get-rich mechanism
€
kη (t) = mt
ti
⎛
⎝ ⎜
⎞
⎠ ⎟
η i /C
Fit-get rich mechanismFit-get rich mechanismThe nodes with higher fitness
increases the connectivity faster
satisfies the condition
€
ki =t
ti
⎛
⎝ ⎜
⎞
⎠ ⎟
f (ε i )
.1
1)(1
)(∫ −=
−e
pd€
f (ε) = e−β (ε − μ )
Mapping to a Bose gasMapping to a Bose gasWe can map the fitness model to a Bose
gas with
– density of states p( );– specific volume v=1;– temperature T=1/.
In this mapping, – each node of energy corresponds to
an energy level of the Bose gas – while each link pointing to a node of
energy , corresponds to an occupation of that energy level.
Network
Energy diagramG. Bianconi, A.-L. Barabási 2001
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1
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6
Bose-Einstein Bose-Einstein condensation in trees condensation in trees scale-free networksscale-free networks
In the network there is a critical temperature Tc such that
•for T>Tc the network is in the
fit-get-rich phase
•for T<Tc the network is in the
winner-takes-all
or Bose-condensate phase
Correlations in the InternetCorrelations in the Internetand the fitness modeland the fitness model
knn (k) mean value of the connectivity of neighbors sites of a node with connectivity k
C(k) average clustering coefficient of nodes with connectivity k.
Vazquez et al. 2002
Growing weighted models
With new nodes arriving at each time
Yook & Barabasi 2001
Barrat et al. 2004With weight-degree
correlations
And possible condensation of the links
G. Bianconi 2005
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Growing Cayley-treeGrowing Cayley-tree
Each node is either at the interface ni=1 or in the bulk ni=0
At each time step a node at the interface is attached to m new nodes with energies from a p( ) distribution.
High energy nodes at the interface are more likely to grow.
The probability that a node i grows is given by
G. Bianconi 2002
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1
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5 6
7
8
9
ii ne i∝Π
Nodes at the interface
Mapping to a Fermi gasMapping to a Fermi gas
The growing Cayley tree network can be mapped into a Fermi gas – with density of states
p();– temperature T=1/;– specific volume v=1-1/m.
In the mapping the nodes corresponds to the energy levels
the nodes at the interface to the occupied energy levels
Chung & Lu 2002, Caldarelli et al. 2002, Park & Newman 2003
Molloy Reed configuration model
Networks with given degree distribution
Assign to each node a degree from the given degree distributionCheck that the sum of stubs is evenLink the stubs randomlyIf tadpoles or double links are
generated repeat the construction
∏ ∑−Σ=
i jiji akGP )(
1)(
1
δ
Molloy & Reed 1995
Caldarelli et al. hidden variable model
Every nodes is associated with an hidden variable xi
The each pair of nodes are linked with probability
€
pij = f (x i, x j )
k(x) = N dy ρ(y) f (x, y)∫
Caldarelli et al. 2002Soderberg 2002Boguna & Pastor-Satorras 2003
Park & Newman Park & Newman Hidden variables modelHidden variables model
J. Park and M. E. J. Newman (2004).
€
H = θ iki = (θ i +θ j )ai, ji, j
∑i
∑
pij =eθ i +θ j
1+ eθ i +θ j
∫+
θρθ−= θ+θ 1
11
'iie
)'('d)N(k
The system is defined through an Hamiltonian
pij is the probability of a link
The “hidden variables” θi are quenched and distributed through the nodes with probability ρθ
There is a one-to-one correspondence between θ and the average connectivity of a node
Random graphs
Binomial Poisson distribution distribution
G(N,p) ensembleG(N,p) ensemble
Graphs with N nodesEach pair of nodes linked
with probability p
G(N,L) ensembleG(N,L) ensemble
Graphs with exactly N nodes and
L links
€
P(k) =N −1
k
⎛
⎝ ⎜
⎞
⎠ ⎟pk (1 − p)N −1−k
P(k) =1
k!c ke−c
Statistical mechanics and
random graphs
Microcanonical Configurations G(N,L) GraphsEnsemble with fixed energy E Ensemble with fixed # of links L
Canonical Configurations G(N,p) GraphsEnsemble with fixed average Ensemble with fixed average energy <E> # of links <L>
Statistical mechanics Random graphs
Gibbs entropy and entropy of the G(N,L) random
graph
))(log( EkS Ω= )log(ZN
1=Σ
)( EΩ
Gibbs Entropy
Statistical mechanicsMicrocanonical ensemble
Random graphsG(N,L) ensemble
Total number of microscopic configurations with energy E
⎟⎟⎠
⎞⎜⎜⎝
⎛ −=
L
NNZ
21 /)(
Entropy per node of the G(N,L)ensemble
Total number of graphs in the G(N,L) ensembles
Shannon entropy and entropy of the G(N,p) random
graph
€
S = − p(E)lnp(E)E
∑
€
S = −1
Np(aij )ln
a ij{ }
∑ p(aij )
€
p(E) =1
Ze−bE
Shannon Entropy
Statistical mechanicsCanonical ensemble
Random graphsG(N,p) ensemble
Typical number of microscopic configurations with temperature €
Σ =−c
2lnc +
N
2ln N −
(N − c)
2ln(N − c)
Entropy per node of the G(N,p)ensemble
Total number of typical graphs the G(N,p) ensembles
Hypothesis:Hypothesis: Real networks are single instances
of an ensemble of possible networks which would equally well perform the function of
the existing network
The “complexity” of a real network is a decreasing function
of the entropy of this ensemble
Complexity of a real networkComplexity of a real network