2. random graphs the erdös-rényi models. distinguish: –equilibrium random networks...
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2. Random Graphs
The Erdös-Rényi models
Distinguish:– Equilibrium random networks– Nonequilibrium random networks
Equilibrium random networks
A classical undirected random graph:– the total number of vertices is fixed– connect randomly chosen pairs of vertices
Nonequilibrium random network
A classical random graph that grows through simultaneous addition of vertices and links– at each time step a new vertex is added– simultaneously, a pair of randomly chosen vertices is
connected
Status
Graph theory:– Equilibrium networks with a Poisson degree
distribution
Physics:– Nonequilibrium (growing networks), percolation
Statistical sense
A particular observed network is only one member of a statistical ensemble of all possible realizationsRandom network -> Statistical ensembleN nodes -> How should we understand the degree distribution? It determines the the ensemble of the equilibrium random networks
2/)1(2 NNpossible graphs
The Erdos-Renyi model
Definition: N labeled nodes connected by n links which are chosen randomly from the N(N-1)/2 possible linksThere are graphs with N nodes and n links
n
NN 2/)1(
Alternative definition
Binomial model: start with N nodes, every pair of nodes being connected with probability pThe total number of links, n, is a random variable– E(n)=pN(N-1)/2
Probability of generating a graph, G0{N,n}
nNN
n ppGP
2
)1(
0 )1()(
Growing a graph
Sometimes we will study properties of the graph as p increasesAssign a random number qi[0,1] to attach links and then links appear as p is increased p> q i
We are interested in the “static” properties of the graph when N-> and keeping constant p or n
N->
Definition: almost every graph has a property Q if the probability of having Q approaches 1 as N-> The main goal of Random Graph theory is to determine at what connection probability p a particular property of a graph most likely arises
Many important properties appear suddenly:– almost everygraph has the property– almost no graph has it
Usually there exists a critical probability pc(N)
p(N) probability that almost every graph has property Q
If p(N) grows slower than pc(N)
If p(N) grows faster than pc(N)
]0)(/)([ Nc NpNp
0)(,lim
QP pNN
])(/)([ Nc NpNp
1)(,lim
QP pNN
Examples
Larger graphs with the same p contain more links since n=pN(N-1)/2– Appearance of cycles can occur for smaller p in large
graphs than in smaller ones [pc(N->)->0]
Average degree of the graph
pNNpNnk )1(/2
Subgraphs
P1 set of nodes, E1 set of links
G1(P1,E1) is a subgraph of G(P,E) if all nodes of G1 belong to G and links too.
Basic subgraphs:– cycles– trees– complete graphs
Evolution of the graph (p grows)
pNGG ,
Subgraph in graph
F small graph of k nodes and l linksHow many subgraphs like F exist in G?
This expected value depends on p. If N>>k
lpa
k
k
NXE
!)(
a
pNXE
lk
)(
There are no subgraphs like FIf = constant => mean number of subgraphs is a finite numberThe critical probability
0)(0)( if / XENNpN
lk
lkc cNNp /)(
Tree of order k: l=k-1Cycle of order k: l=kComplete subgraph l=k(k-1)/2
We can see how the subgraphs appear when increasing p
Mean connectivity
<k>=pNIf then <k> is a constantIf 0< <k> < 1 almost surely all clusters are either trees or clusters containing exactly one cycleAt <k>=1 the structure changes abruptly. Cycles appear and a giant cluster develops
1 Np
Degree distribution
The degree of a node follows a binomial distribution (in a random graph with p)
Probability that a given node has a connectivity kFor large N, Poisson distribution
kNki pp
k
NkkP
1)1(
1)(
!!
)()(
k
ke
k
pNekP
kk
kpN
Mean short path
Assume that the graph is homogeneousThe number of nodes at distance l are <k> l
How to reach the rest of the nodes?lrand to reach all nodes => kl=N
pN
N
k
Nl
ln
ln
ln
lnrand
Clustering coefficient
Probability that two nodes are connected (given that they are connected to a third)?
N
kpCrand
Nk
Crand 1
while it is constant for real networks
Spectrum: random matrices
If Aij real, symmetric, NxN uncorrelated random matrix <Aij>=0 and <Aij
2>=2
Density of eigenvalues of
Wigner’s or semicircle law (late 50’s)
seN
otherwi0
2if)2(
4)( 2
22
NA /
Spectrum: random graph
<Aij>= not 0
2 =p(1-p)Plotting
() semicircel law as N increases (p constant)
)1( vs)1(
pNppNp
In general,z<1:– semicircle law– exists an infinite cluster 1 (principal, largest) is isolated, grows like N
z<1: most of the graphs are trees (odd moment vanish). The spectral density contains the weighted sum of the spectral densities of all finite graphs
zcNNp )(
Generalized random graphs
One can construct a graph introducing the degree distribution as an input
How do the properties of the network change with the exponent? decreases from to 0
kkP )(
<k>=kmax-+2 (kmax <N, max degree)
The infinite cluster emerges when
There exists a value 0=3.47875.....
>0 disconnected
>0 almost surely connected
1
0)()2(k
KPkkQ
Exponential cutoff (observed in real world networks)Normalitzable for any k>2 disconnected<2 connected
1 )( / keCkkP k
NON-RANDOM aspects of the topology of real networks
Growing networks
See hand-written notes
Scaling