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Transcript of Models Slides
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Classical Complex
Network Models
Sergio Gmez Universitat Rovira i Virgili, Tarragona (Spain)
Mediterranean School of Complex Networks
Salina 2014
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Outline
Models
Regular networks
Complex network models
Concluding remarks
Classical complex network models
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Outline
Models
Regular networks
Complex network models
Concluding remarks
Classical complex network models
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Models
Models
For networks
For processes on networks
For processes of networks
Why models?
We have data of real networks!
We know the details of real processes on real networks!
We know the evolution of real networks!
Models
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Why models? Because...
Data does not imply knowledge!
Models
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Why models? Because...
Data does not imply knowledge!
Models provide
Explanation
Prediction
Models
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Why models? Because...
Data does not imply knowledge!
Models provide
Explanation
Prediction
Models must be
Simple (Occams razor)
Accurate
Models
Ptolemy Tycho Brahe
Kepler
Copernicus
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Why models? Because...
Data does not imply knowledge!
Models provide
Explanation
Prediction
Models must be
Simple (Occams razor)
Accurate
Note: Agent-based models
Not simple, but more realistic
Difficult (even impossible) to identify relationships
Usually cannot provide explanations, even if accurate and
with good predictions
Models
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Network models
To explain the appearance of topological features
Power-law degree distribution, small-world property,
clustering, community structure, etc.
Models
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Network models
To explain the appearance of topological features
Power-law degree distribution, small-world property,
clustering, community structure, etc.
To investigate the relationship between topology and
function
Which features are needed for a certain phenomenon?
Models
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Network models
To explain the appearance of topological features
Power-law degree distribution, small-world property,
clustering, community structure, etc.
To investigate the relationship between topology and
function
Which features are needed for a certain phenomenon?
To understand processes on networks
Diffusion, epidemic spreading, rumors, innovations,
collaboration, competition, evolutionary games, routing,
congestion, synchronization, etc.
Models
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Network models
To explain the appearance of topological features
Power-law degree distribution, small-world property,
clustering, community structure, etc.
To investigate the relationship between topology and
function
Which features are needed for a certain phenomenon?
To understand processes on networks
Diffusion, epidemic spreading, rumors, innovations,
collaboration, competition, evolutionary games, routing,
congestion, synchronization, etc.
To understand processes of networks
Network formation, evolution, interaction, etc.
Models
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Example
Models
Network model
SF
Dynamics model
Kuramoto
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Example
Models
Real network
Star (proxy of SF)
Real dynamics
Rssler chaotic oscillator
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Outline
Models
Regular networks
Complex network models
Concluding remarks
Classical complex network models
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Regular networks
Traditional networks used in physics and
engineering
Sometimes allow analytical solutions
Discretization of continuous space
Regular networks
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Toy networks
Line
Star
Ring
Fully connected
Regular networks
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Lattices
Regular networks
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Lattices
Without and with periodic boundary conditions
Regular networks
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Other
Bethe lattices / Cayley trees (internal nodes of same k)
Ramanujan graphs (large spectral gap)
Regular networks
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Other
Fractal networks
Regular networks
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Other
Apollonian networks
Regular networks
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Outline
Models
Regular networks
Complex network models
Concluding remarks
Classical complex network models
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Complex network models
Models for these properties
Degree distribution
Average path length
Clustering
Communities
Other
Complex network models
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Models according to degree distribution
Erds-Rnyi model (ER)
Barabsi-Albert model (BA)
Configuration model (CM)
Interpolating model between ER and BA
Complex network models
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Models according to degree distribution
Erds-Rnyi model (ER)
Barabsi-Albert model (BA)
Configuration model (CM)
Interpolating model between ER and BA
Complex network models
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Erds-Rnyi model (ER)
Model GN,K by Erds & Rnyi (1959)
N: number of nodes
K: number of edges (0 K N(N-1)/2)
Each edge connects a randomly selected (and not previously
connected) pair of nodes
Model GN,p by Gilbert (1959)
N: number of nodes
p: probability of having an edge (0 p 1)
Each pair of nodes has a probability p of having an edge
Complex network models
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Erds-Rnyi model (ER)
Model GN,p
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Complex network models
p=0.01
p=0.02 p=0.03
p=0.00
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Erds-Rnyi model (ER)
Relationship between GN,K and GN,p
GN,p to GN,K
GN,K to GN,p
Property
Almost surely, connected network if
Complex network models
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Erds-Rnyi model (ER)
Degree distribution
Binomial
Poisson, in the limit while constant
Complex network models
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Models according to degree distribution
Erds-Rnyi model (ER)
Barabsi-Albert model (BA)
Configuration model (CM)
Interpolating model between ER and BA
Complex network models
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Barabsi-Albert model (BA)
Based on growth and preferential attachment (1999)
N: number of nodes
m0: number of initial nodes (m0 N)
m: number of edges for each new node (m m0)
The network begins with an initial small connected network
containing m0 nodes
New nodes are added until the network has the desired N
nodes (growth)
Each new node establishes m edges to the current available
nodes
The probability pi that each of the m edges connects to node i
is proportional to its current number of links ki (preferential
attachment)
Complex network models
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Barabsi-Albert model (BA)
Degree distribution
Power-law (scale-free) with exponent = 3
Complex network models
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Barabsi-Albert model (BA)
Note
Both growth and preferential attachment are needed to obtain
the SF degree distribution
History
Yule (1925): preferential attachment to obtain SF degree
distribution
Simon (1955): application of modern master equation method
Price (1976): application to the growth of networks
Barabsy & Albert (1999): rediscovery, name of preferential
attachment, popularity
Complex network models
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Barabsi-Albert model (BA)
Many variations
Non-linear preferential attachment
Dynamic edge rewiring
Fitness models
Hierarchic growing
Deterministic growing
Local growing
Accelerating growth
Complex network models
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Models according to degree distribution
Erds-Rnyi model (ER)
Barabsi-Albert model (BA)
Configuration model (CM)
Interpolating model between ER and BA
Complex network models
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Configuration model (CM)
Build network given degree sequence
N: number of nodes
P(k): degree distribution
Assign a random degree ki to each node according to the
given degree distribution P(k)
Create a vector with all the slots (half edges)
Connect randomly pairs of slots
Could be used to rewire networks
Could be used to generate networks with scale-free (SF)
degree distribution for any value of the exponent > 2
Complex network models
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Configuration model (CM)
Algorithm
Complex network models
Random assignment
of degrees according
to P(k)
Vector of slots
Random permutation
of the vector of slots
Connection of slots
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Configuration model (CM)
Details
The number of slots must be even (2L)
Probability pij of connecting nodes i and j
Multi-edges and self-loops may appear
Complex network models
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Configuration model (CM)
Details
For large N, the number of multi-edges and self-loops is
negligible if and are finite
For scale-free degree distributions , the second
moment diverges if the exponent is (2,3]
Modify the algorithm to avoid the presence of multi-edges and
self-loops
Introduce a cut-off in P(k) scaling as
Algorithms to find a random permutation
FisherYates shuffle (1938)
Durstenfeld (1964) / Knuth (1969)
Complex network models
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Configuration model (CM)
Generalization
Configuration model to build networks with desired degree-
degree correlations P(k,k)
Main developers and theory
Bekessy, Bekessy & Komlos (1972)
Bender & Canfield (1978)
Bollobs (1980)
Wormald (1981)
Molloy & Reed (1995)
Complex network models
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Models according to degree distribution
Erds-Rnyi model (ER)
Barabsi-Albert model (BA)
Configuration model (CM)
Interpolating model between ER and BA
Complex network models
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Interpolating model between ER and BA
Model by Gmez-Gardees & Moreno (2006)
N: number of nodes
m0: number of initial nodes (m0 N)
U(0): set of initially unconnected nodes
m: number of links for each selected node (m m0)
: probability of generating an ER edge
Initial small fully connected network containing m0 nodes
N- m0 randomly selected nodes in U(0) establish m edges to
the rest of the nodes
For each edge of the selected node
With probability the destination is chosen with uniform probability among the rest N-1 nodes (ER edge)
With probability 1- the destination is selected with preferential attachment (BA edge)
Complex network models
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Interpolating model between ER and BA
Note
The preferential attachment (PA) probability depends only on
the links generated by the PA sub-procedure
Two different sub-models to define these PA probabilities
Degree distribution
= 1: ER network, binomial (Poisson) P(k)
= 0: BA network, SF with exponent = 3
0 < < 1: Interpolating network
BA ER
0 1
Complex network models
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Models according to average path length
Watts-Strogatz model (WS)
Complex network models
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Watts-Strogatz model (WS)
Based on regular network and random rewirings (1998)
N: number of nodes
k: mean degree, even integer (k < N)
p: rewiring probability
Nodes initially in a regular ring lattice
Each node connects to its k nearest neighbors, k / 2 on each
side (clockwise and counterclockwise)
For each node, rewire each clockwise original edge with
probability p to a new random destination (multi-edges and
self-loops not allowed)
Regular ER
0 p 1
Complex network models
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Watts-Strogatz model (WS)
Scheme
Complex network models
p=0.0
p=0.9 p=0.5
p=0.2 p=0.1
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Watts-Strogatz model (WS)
Average path length and clustering
Complex network models
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Watts-Strogatz model (WS)
Conclusion
Long range edges explain Small-World property
Variants
Adding long range edges instead of rewiring
Different initial regular networks
Complex network models
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Models according to clustering
Serrano-Bogu model
Hidden hyperbolic space model
Complex network models
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Models according to clustering
Serrano-Bogu model
Hidden hyperbolic space model
Complex network models
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Serrano-Bogu model
Model based on Configuration Model (2005)
N: number of nodes
P(k): degree distribution
c(k): clustering distribution
Assignment of degrees to nodes according to P(k)
Assignment of number of triangles to each degree class
according to c(k)
Triangle formation
Closure of the network
The algorithm preserves both degree and clustering
distributions
Complex network models
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Serrano-Bogu model
Results
clustering distribution degree distribution
Complex network models
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Models according to clustering
Serrano-Bogu model
Hidden hyperbolic space model
Complex network models
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Hidden hyperbolic space model
Model by Krioukov, Papadopoulos, Vahdat & Bogu
(2009)
N: number of nodes
P(k): degree distribution
c(k): clustering distribution
The network is embedded in a hidden 2D hyperbolic space
The hidden space curvature affects the degree distribution
The temperature of the model affects the clustering
distribution
The model may be used for local routing
Complex network models
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Hidden hyperbolic space model
Results
Complex network models
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Models according to communities
Planted partition model
Lancichinetti-Fortunato-Radicchi model (LFR)
Complex network models
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Models according to communities
Planted partition model
Lancichinetti-Fortunato-Radicchi model (LFR)
Complex network models
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Planted partition model
Model by Condon & Karp (2001)
n: number of communities
m: number of nodes per community
pin: probability of having an edge inside a community
pout: probability of having an edge between communities
The probability of having and edge between each pair of
nodes is
pin if they belong to the same community
pout if they belong to different communities
Based on ER model GN,p
zin = pin (m - 1): expected internal degree
zout = pout m (n - 1): expected external degree
Complex network models
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Planted partition model
Girvan-Newman model (2002) is a particular case with
n = 4
m = 32
= zin + zout = 16
zin = 15 zin = 11
Complex network models
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Models according to communities
Planted partition model
Lancichinetti-Fortunato-Radicchi model (LFR)
Complex network models
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Lancichinetti-Fortunato-Radicchi model (LFR)
Model based on planted partition model and
configuration model (2008)
1: exponent of the SF degree distribution
2: exponent of the SF distribution of community sizes
: mixing parameter, fraction of links to other communities
Generation of the community sizes according to 2
Generation of the degrees of the nodes according to 1 Creation and random assignment of the stubs for the internal
edges
Creation and random assignment of the stubs for the external
edges
Complex network models
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Lancichinetti-Fortunato-Radicchi model (LFR)
Example
Complex network models
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Lancichinetti-Fortunato-Radicchi model (LFR)
Variant
Extension by Lancichinetti & Fortunato (2009) for weighted
and directed networks with overlapping communities
Complex network models
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Other models
Models for hierarchical networks
Models for multiplex networks
Complex network models
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Other models
Models for hierarchical networks
Models for multiplex networks
Complex network models
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Models for hierarchical networks
Hierarchical model by Ravasz & Barabsi (2003)
RB25 and RB125
Complex network models
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Models for hierarchical networks
Two-level planted partitions by Arenas, Daz-Guilera &
Prez-Vicente (2006)
H13-4-1
Complex network models
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Other models
Models for hierarchical networks
Models for multiplex networks
Complex network models
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Models for multiplex networks
Multiplex networks
Edges of different classes: one layer per class of edge
The same nodes present in all layers
Models
Combinations of models for single layers (ER, BA, etc.)
Correlation between layers, e.g. degree correlation
(assortative, dissortative, random), community correlations,
etc.
Complex network models
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Outline
Models
Regular networks
Complex network models
Concluding remarks
Classical complex network models
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Concluding remarks
We need complex network models
Many models available to account for different
properties
Most important ones: ER, BA, CM, WS
New models needed for new network paradigms
(multiplex, interconnected, time-varying, etc.)
Classical complex network models
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Thank you for your attention!
Contact [email protected]
http://deim.urv.cat/~sergio.gomez
Classical complex network models