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Scaling, renormalization and self-similarity in complex networks

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Scaling, renormalization and self-similarity in complex networks Hernan A. Makse Levich Institute and Physics Dept. City College of New York Chaoming Song (CCNY) – PowerPoint PPT presentation

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Title: Scaling, renormalization and self-similarity in complex networks


1
Scaling, renormalization and self-similarity in
complex networks
Chaoming Song (CCNY) Lazaros Gallos (CCNY) Shlomo
Havlin (Bar-Ilan, Israel)
Protein interaction network
2
Are scale-free networks really free-of-scale?
If you had asked me yesterday, I would have said
surely not - said Barabasi. (Science News,
February 2, 2005).
Small World effect shows that distance between
nodes grows logarithmically with N (the network
size) OR Self-similar fractal topology is
defined by a power-law relation
Small world contradicts self-similarity!!!
How the network behaves under a scale
transformation.
3
WWW
nd.edu
300,000 web-pages
4
Internet
Faloutsos et al., SIGCOMM 99
Internet connectivity, with selected backbone
ISPs (Internet Service Provider) colored
separately.
5
Yeast Protein-Protein Interaction Map

Individual proteins Physical interactions from
the filtered yeast interactome database 2493
high-confidence interactions observed by at least
two methods (yeast two-hybrid). 1379 proteins,
ltkgt 3.6
J. Han et al., Nature (2004)
Modular structure according to function!
Colored according to protein function in the
cell Transcription, Translation, Transcription
control, Protein-fate, Genome maintenance,
Metabolism, Unknown, etc
from MIPS database, mips.gsf.de
6
Metabolic network of biochemical reactions in
E.coli

Chemical substrates Biochemical interactions
enzyme-catalyzed reactions that transform one
metabolite into another.
J. Jeong, et al., Nature, 407 651 (2000)
Modular structure according to the
biochemical class of the metabolic products of
the organism.
Colored according to product class Lipids,
essential elements, protein, peptides and amino
acids, coenzymes and prosthetic groups,
carbohydrates, nucleotides and nucleic acids.
7
How long is the coastline of Norway?
It depends on the length of your ruler.
Fractals look the same on all scales
scale-invariant.
Box length
Fractal Dimension dB- Box Covering Method
Total no. of boxes
8
Boxing in Biology
Boxing in Biology
How to zoom out of a complex network?
  • Generate boxes where all
  • nodes are within a distance
  • Calculate number of boxes, ,
  • of size needed to cover the
  • network

We need the minimum number of boxes NP-complete
optimization problem!
9
Most efficient tiling of the network
8 node network Easy to solve
4 boxes
5 boxes
300,000 node network Mapping to graph colouring
problem. Greedy algorithm to find minimum boxes
10
Larger distances need fewer boxes
1
2
-dB
fractal
log(NB)
3
non fractal
log(lB)
11
Box covering in yeast protein interaction network
12
Most complex networks are Fractal
Biological networks
Metabolic
Protein interaction
43 organisms - all scale
Three domains of life archaea, bacteria, eukaria
E. coli, H. sapiens, yeast
Song, Havlin, Makse, Nature (2005)
13
Metabolic networks are fractals
14
Technological and Social Networks TOO
WWW
Hollywood film actors
212,000 actors
Other bio networks Khang and Bremen groups
Internet is not fractal!
nd.edu domain
300,000 web-pages
15
Two ways to calculate fractal dimensions
Cluster growing method
Box covering method
In homogeneous systems (all nodes with similar k)
both definitions agree
percolation
16
Box Covering flat average
Cluster Growing biased
exponential
power law
Different methods yield different results due to
heterogeneous topology
Box covering reveals the self similarity. Cluster
growth reveals the small world. NO
CONTRADICTION! SAME HUBS ARE USED MANY TIMES IN
CG.
17
Is evolution of the yeast fractal?
present day
Other Fungi
Animals Plants
Archaea Bacteria
Yeast
300 million years ago
Ancestral yeast
Ancestral Fungus
Ancestral Eukaryote
1 billion years ago
Ancestral Prokaryote Cell
3.5 billion years ago
Following the phylogenetic tree of life
COG database von Mering, et al Nature (2002)
1.5 billion years ago
18
Same fractal dimension and scale-free exponent
over 3.5 billion years
Suggests that present-day networks could have
been created following a self-similar, fractal
dynamics.
19
Renormalization in Complex Networks
NOW, REGARD EACH BOX AS A SINGLE NODE AND ASK
WHAT IS THE DEGREE DISRIBUTION OF THE
NETWORK OF BOXES AT DIFFERENT SCALES ?
20
Renormalization of WWW network with
21
The degree distribution is invariant under
renormalization
Internet is not fractal dB--gt infinity But it is
renormalizable
22
Turning back the time
Repeatedly BOXING the network is the same as
going back in time from a single
node to present day.
THE RENORMALIZATION SCHEME
renormalization
present day network
ancestral node
1
time evolution
Can we predict the past. ? if not the future.
23
Evolution of complex networks
time evolution
24
How does Modularity arise?
The boxes have a physical meaning self-similar
nested communities
How to identify communities in complex networks?
renormalization
present day network
ancestral node
1
time evolution
25
Emergence of Modularity in PIN
Boxes are related to the biologically relevant
functional modules in the yeast protein
interactome
renormalization
time evolution
translation
transcription
protein-fate
cellular-fate organization
present day network
26
Emergence of modularity in metabolic networks
Appearance of functional modules in E. coli
metabolic network. Most robust network than
non-fractals.
27
Theoretical approach
How the communities/modules are linked?
renormalization
k2
k8
s1/4
k degree of the nodes
k degree of the communities
28
Theoretical approach to modular networks Scaling
theory to the rescue
The larger the community the smaller their
connectivity
new exponent describing how families link
29
Scaling relations
A theoretical prediction relating the different
exponents
distance
boxes
degree
30
Scaling relations
The communities also follow a self-similar pattern
WWW
Metabolic
prediction
Scaling relation works
scale-free
fractals
communities/modules
31
Why fractality?
Some real networks are not fractal
INTERNET
32
What is the origin of self-similarity?
Can you see the difference?
FRACTAL
NON FRACTAL
Internet map
Yeast protein map
E.coli metabolic map
HINT the key to understand fractals is in the
degree correlations P(k1,k2) not in P(k)
33
Quantifying correlations
P(k1,k2) Probability to find a node with k1
links connected with a node of k2 links
Internet map - non fractal
Metabolic map - fractal
high prob.
low prob.
log(k2)
log(k2)
P(k1,k2)
low prob.
high prob.
log(k1)
log(k1)
Hubs connected with hubs
Hubs connected with non-hubs
34
Quantify anticorrelation between hubsat all
length scales
Hub-Hub Correlation function fraction of hub-hub
connections
hubs
Renormalize
hubs
Hubs connected directly
35
Hub-hub connection organized in a self-similar way
non-fractal
The larger de implies more anticorrelation
fractal
(fractal) (non-fractal)
Anticorrelations are essential for fractal
structure
36
What is the origin of self-similarity?
Non-fractal networks
Fractal networks
  • very compact networks
  • hubs connected with other hubs
  • strong hub-hub attraction
  • assortativity
  • less compact networks
  • hubs connected with non-hubs
  • strong hub-hub repulsion
  • dissasortativity

Internet All available models BA model,
hierarchical random scale free, JKK, etc
WWW, PIN, metabolic, genetic, neural networks,
some sociological networks
37
How to model it? renormalization reverses
time evolution
Song, Havlin, Makse, Nature Physics, 2006
Both mass and degree increase exponentially with
time
time
offspring nodes attached to their parents
renormalize
(m2) in this case
Scale-free
Mode II
Mode I
38
How does the length increase with time?
Mode I NONFRACTAL SMALL WORLD
Mode II FRACTAL
39
Combine two modes together
Mode I with probability e Mode II with
probability 1-e
time
renormalize
e0.5
40
Predictions
Model reproduces local small world, scale-free
and fractality
yeast
h.sapiens
  • model with e0.2
  • repulsion between hubs leads to fractal topology
  • small world locally inside well defined
    communities
  • model with e1
  • attraction between hubs
  • non-fractal
  • small world globally

41
The model reproduces the main features of real
networks
Case 1 e 0.8 FRACTALS Case 2 e 1.0
NON-FRACTALS
42
Model predicts all exponents in terms of growth
rates
Each step the total mass scales with a constant
n, all the degrees scale with a constant s. The
length scales with a constant a, we obtain
We predict the fractal exponents
43
Time evolution in yeast network
44
Multiplicative and exponential growth in yeast
PIN Length-scales, number of conserved proteins
and degree
45
A new principle of network dynamics
less vulnerable to intentional attacks
46
Summary
  • In contrast to common belief, many real world
    networks are self-similar.
  • FRACTALS WWW, Protein interactions, metabolic
    networks, neural networks, collaboration
    networks.
  • NON-FRACTALS Internet, all models.
  • Communities/modules are self-similar, as well.
  • Scaling theory describes the dynamical
    evolution.
  • Boxes are related to the functional modules in
    metabolic and protein networks.
  • Origin of self similarity anticorrelation
    between hubs
  • Fractal networks are less vulnerable than
    non-fractal networks

Positions available jamlab.org
47
An finally, a model to put all this together
A multiplicative growth process of the number of
nodes and links
m 2
Analogous to duplication/divergence mechanism in
proteins??
Probability e hubs always connected strong hub
attraction should lead to non-fractal
Probability 1-e hubs never connected strong hub
repulsion should lead to fractal
48
Different growth modes lead to different topologie
s
For the both models, each step the total number
of nodes scale as n 2m 1( N(t1) nN(t) ).
Now we investigate the transformation of the
lengths. They show quite different ways for this
two models as following
Mode I L(t1) L(t)2
Then we lead to two different scaling law of N L
smaller
smaller
Mode II L(t1) 2L(t)1
Mode III L(t1) 3L(t)
49
Dynamical model
Suppose we have e probability to have mode I, 1-e
probability to have mode II and mode III. Then we
have
or
50
Graph theoretical representation of a
metabolic network
(a) A pathway (catalyzed by Mg2-dependant
enzymes). (b) All interacting metabolites are
considered equally. (c) For many biological
applications it is useful to ignore co-factors,
such as the high energy-phosphate donor ATP,
which results in a second type of mapping that
connects only the main source metabolites to the
main products.
51
Classes of genes in the yeast proteome
52
Renormalization following the phylogenetic tree
P. Uetz, et al. Nature 403 (2000).
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