Introduction to Network Science

Introduction to Network Science

Riadh DHAOU based on the lecture of

Albert-László Barabási

FROM SADDAM HUSSEIN TO NETWORK THEORY

Section 2:

Network Science: Introduction

Network Science: Introduction

A SIMPLE STORY (1) The fate of Saddam and network science

Network Science: Introduction

Thex

Network Science: Introduction

The capture of Saddam Hussein:

shows the strong predictive power of networks.

underlies the need to obtain accurate maps of the networks we aim to study;

and the often heroic difficulties we encounter during the mapping process.

demonstrates the remarkable stability of these networks: The capture of Hussein was not based on fresh intelligence, but rather on his pre-invasion social links, unearthed from old photos stacked in his family album.

shows that the choice of network we focus on makes a huge difference: the hierarchical tree, that captured the official organization of the Iraqi government, was of no use when it came to Saddam Hussein's whereabouts.

A SIMPLE STORY (1) The fate of Saddam and network science

VULNERABILITY DUE TO INTERCONNECTIVITY

Section 3

Network Science: Introduction

Thex

Network Science: Introduction

A SIMPLE STORY (2): August 15, 2003 blackout.

August 14, 2003: 9:29pm EDT 20 hours before

August 15, 2003: 9:14pm EDT 7 hours after

Thex

Network Science: Introduction

A SIMPLE STORY (2): August 15, 2003 blackout.

An important theme of this class:

we must understand how network structure affects the robustness of a complex system.

develop quantitative tools to assess the interplay between network

structure and the dynamical processes on the networks, and their impact on failures.

We will learn that failures reality failures follow reproducible laws, that can be quantified and even predicted using the tools of network science.

NETWORKS AT THE HEART OF COMPLEX SYSTEMS

Section 4

Network Science: Introduction

[adj., v. kuh m-pleks, kom-pleks; n. kom-pleks]

–adjective 1.

composed of many interconnected parts;

compound; composite: a complex highway system.

2.

characterized by a very complicated or involved arrangement of parts, units, etc.:

complex machinery.

3.

so complicated or intricate as to be hard to understand or deal with: a complex problem.

Source: Dictionary.com

COMPLEX SYSTEMS

Complexity, a scientific theory which asserts that some systems display

behavioral phenomena that are completely inexplicable by any conventional analysis of the systems’ constituent parts. These phenomena, commonly referred to as emergent behaviour, seem to occur in many complex systems involving living organisms, such as a stock market or the human brain.

Source: John L. Casti, Encyclopædia Britannica

Network Science: Introduction

THE ROLE OF NETWORKS

Behind each complex

system there is a network, that defines the interactions between the component.

Network Science: Introduction

Keith Shepherd's "Sunday Best”. http://baseballart.com/2010/07/shades-of-greatness-a-story-that-needed-to-be-told/

The “Social Graph” behind Facebook

SOCIETY

Network Science: Introduction

: departments : consultants : external experts

www.orgnet.com

STRUCTURE OF AN ORGANIZATION

Network Science: Introduction

Brain

Human Brain has between 10-100 billion neurons.

BRAIN

Network Science: Introduction

The subtle financial networks

Network Science: Introduction

The not so subtle financial networks: 2011

Network Science: Introduction

Nodes:

Links:

http://ecclectic.ss.uci.edu/~drwhite/Movie

BUSINESS TIES IN US BIOTECH-INDUSTRY

Companies Investment Pharma

Research Labs Public

Biotechnology

Collaborations Financial R&D

Network Science: Introduction

INTERNET

domain2

domain1

domain3 router

Network Science: Introduction

Drosophila Melanogaster Homo

Sapiens

In the generic networks shown, the points

represent the elements of each organism’s genetic network, and the dotted lines show the

interactions between them.

HUMANS GENES

Network Science: Introduction

Complex systems

Made of many non-identical elements connected by diverse interactions.

NETWORK

HUMANS GENES

Drosophila Melanogaster Homo

Sapiens

Network Science: Introduction

THE ROLE OF NETWORKS

Network Science: Introduction

Behind each system studied in complexity there is an intricate wiring diagram, or a network, that defines the interactions between the component.

We will never understand complex system unless we map out and understand

the networks behind them.

TWO FORCES HELPED THE EMERGENCE OF NETWORK

SCIENCE

Section 5

Network Science: Introduction

Graph theory: 1735, Euler

Social Network Research: 1930s, Moreno Communication networks/internet: 1960s Ecological Networks: May, 1979.

THE HISTORY OF NETWORK ANALYSIS

Network Science: Introduction

THE HISTORY OF NETWORK ANALYSIS

Network Science: Introduction

(Random Networks in Graph Theory)

(Social Network)

The emergence of network maps:

Network Science: Introduction

THE EMERGENCE OF NETWORK SCIENCE

Movie Actor Network, 1998;

World Wide Web, 1999.

C elegans neural wiring diagram 1990 Citation Network, 1998

Metabolic Network, 2000;

Protein-protein Interaction (PPI) network, 2001

The universality of network characteristics:

Network Science: Introduction

THE EMERGENCE OF NETWORK SCIENCE

The architecture of networks emerging in various domains of

science, nature, and technology are

more similar to each other than one

would have expected.

THE CHARACTERISTICS OF NETWORK SCIENCE

Section 6

Network Science: Introduction

Interdisciplinary

Quantitative and Mathematical Computational

Empirical

Network Science: Introduction

THE CHARACTERISTICS OF NETWORK SCIENCE

Interdisciplinary

Quantitative and Mathematical Computational

Empirical, data driven

Network Science: Introduction

THE CHARACTERISTICS OF NETWORK SCIENCE

Interdisciplinary

Quantitative and Mathematical Computational

Empirical

Network Science: Introduction

THE CHARACTERISTICS OF NETWORK SCIENCE

Interdisciplinary

Quantitative and Mathematical Computational

Empirical

Network Science: Introduction

THE CHARACTERISTICS OF NETWORK SCIENCE

THE IMPACT OF NETWORK SCIENCE

Section 7

Network Science: Introduction

Google

Market Cap

:

$189 billion

Cisco Systems

networking gear Market cap

:

$112 billion

Facebook market cap:

$50 billion

www.bizjournals.com/austin/news/2010/11/

15/facebooks... - Cached

Network Science: Introduction

ECONOMIC IMPACT

Reduces

Inflammation Fever

Pain

Prevents Heart attack Stroke

Causes Bleeding Ulcer Reduces the risk of

Alzheimer's Disease

COX2

Reduces the risk of breast cancer

ovarian cancers colorectal cancer DRUG DESIGN, METABOLIC ENGINEERING:

Network Science: Introduction

DRUG DESIGN, METABOLIC ENGINEERING:

HUMAN DISEASE NETWORK

Network Science: Introduction

The network behind a military engagement

Thex

Predicting the H1N1 pandemic

Network Science: Introduction

Thex

In September 2010 the National Institutes of Health awarded $40 million to researchers at Harvard, Washington University in St. Louis, the University of Minnesota and UCLA, to develop the technologies that could

systematically map out brain circuits.

The Human Connectome Project (HCP) with the ambitious goal to construct a map of the complete structural and functional neural connections in vivo within and across individuals.

http://www.humanconnectomeproject.org/overview/

Network Science: Introduction

BRAIN RESEARCH

Barabasi Lab

Management

Barabasi Lab

Barabasi Lab

SCIENTIFIC IMPACT

Section 8

Network Science: Introduction

NETWORK SCIENCE The science of the 21

century

Network Science: Introduction Years

Times cited

SUMMARY

Section 9

Network Science: Introduction

Thex

Network Science: Introduction

NGRAMS Networks Awareness

Thex

If you were to understand the spread of diseases, can you do it without networks?

If you were to understand the WWW structure,

searchability, etc, hopeless without invoking the Web’s topology.

If you want to understand human diseases, it is hopeless without considering the wiring

diagram of the cell.

Network Science: Introduction

MOST IMPORTANT Networks Really Matter

Some Graph Properties

Section 10

Network Science: Introduction

Degree distribution

P(k): probability that a randomly chosen node has degree k

Nk = # nodes with degree k P(k) = Nk / N plot

DEGREE DISTRIBUTION

The maximum number of links a network of N nodes can have is:

2



 

 = N(N −1) 2

A graph with degree L=L

is called a complete graph, and its average degree is <k>=N-1

Network Science: Graph Theory

COMPLETE GRAPH

Most networks observed in real systems are sparse:

L << L

or

<k> <<N-1.

WWW (ND Sample): N=325,729; L=1.4 10⁶ L_max=10¹² <k>=4.51 Protein (S. Cerevisiae): N= 1,870; L=4,470 L_max=10⁷ <k>=2.39 Coauthorship (Math): N= 70,975; L=2 10⁵ L_max=3 10¹⁰ <k>=3.9 Movie Actors: N=212,250; L=6 10⁶ L_max=1.8 10¹³ <k>=28.78

(Source: Albert, Barabasi, RMP2002)

Network Science: Graph Theory

REAL NETWORKS ARE SPARSE

ADJACENCY MATRICES ARE SPARSE

Network Science: Graph Theory

bipartite graph

Examples:

Hollywood actor network Collaboration networks

Disease network (diseasome)

BIPARTITE GRAPHS

Network Science: Graph Theory

Gene network

GENOME

PHENOME DISEASOME

Disease network

Goh, Cusick, Valle, Childs, Vidal & Barabási, PNAS (2007)

GENE NETWORK – DISEASE NETWORK

Network Science: Graph Theory

HUMAN DISEASE NETWORK

Y.-Y. Ahn, S. E. Ahnert, J. P. Bagrow, A.-L. Barabási 
Flavor network and the principles of food pairing , Scientific Reports 196, (2011).

Ingredient-Flavor Bipartite Network

Network Science: Graph Theory

Clustering coefficient

Section 11

Clustering coefficient:

what fraction of your neighbors are connected?

Node i with degree ki

Ci in [0,1]

Network Science: Graph Theory

CLUSTERING COEFFICIENT

Watts & Strogatz, Nature 1998.

Clustering coefficient:

what fraction of your neighbors are connected?

Node i with degree ki

Ci in [0,1]

Network Science: Graph Theory

CLUSTERING COEFFICIENT

Watts & Strogatz, Nature 1998.

Random Networks

Section 12

RANDOM NETWORK MODEL

The random network model

Section 12.1

Erdös-Rényi model (1960) Connect with probability p

p=1/6 N=10

<k> ~ 1.5 Pál Erdös

(1913-1996)

Alfréd Rényi (1921-1970)

RANDOM NETWORK MODEL

RANDOM NETWORK MODEL

Network Science: Random

Definition:

A random graph is a graph of N nodes where each pair of nodes is connected by probability p.

RANDOM NETWORK MODEL

p=1/6 N=12

L=8 L=10 L=7

RANDOM NETWORK MODEL

p=0.03 N=100

The number of links is variable

Section 12.2

RANDOM NETWORK MODEL

p=1/6 N=12

L=8 L=10 L=7

Number of links in a random network

P(L): the probability to have exactly L links in a network of N nodes and probability p:

Network Science: Random Graphs

P(L) =

N 2



  

  L





 







 

 p

(1 − p )

N(N−1)

2 −L

The maximum number of links in a network of N nodes.

Number of different ways we can choose L links among all potential links.

Binomial distribution...

MATH TUTORIAL Binomial Distribution: The bottom line

Network Science: Random Graphs

http://keral2008.blogspot.com/2008/10/derivation-of-mean-and-variance-of.html

P( x) = N x



  

  p

(1 − p )

< x >= Np

< x

>= p (1 − p )N + p

N

σ

= ( < k

> − < k >

)

= [ p (1 − p ) N]

RANDOM NETWORK MODEL

P(L): the probability to have a network of exactly L links

Network Science: Random Graphs

P(L) =

N 2



  

  L





 







 

 p

(1 − p )

N(N−1)

2 −L

< L >= LP(L) = pN(N−1)

L=0 2

N(N−1)

∑

•The average number of links <L> in a random graph

•The standard deviation

σ

= p (1 − p) N(N − 1) 2

< k >= 2L / N = p ( N − 1)

Degree distribution

Section 12.3

DEGREE DISTRIBUTION OF A RANDOM GRAPH

Network Science: Random Graphs

As the network size increases, the distribution becomes increasingly narrow—we are increasingly confident that the degree of a node is in the vicinity of <k>.

Select k

nodes from N-1 probability of having k edges

probability of missing N-1-k edges

P(k) = N −1 k



 

p^k(1− p)^(N−1)−k

< k >= p (N − 1) σ

= p (1 − p )( N − 1)

σ

< k > = 1 − p p

1 ( N − 1)



  

 

1/ 2

≈ 1

(N − 1)

DEGREE DISTRIBUTION OF A RANDOM GRAPH

Network Science: Random Graphs

P(k) = N −1 k



 

p^k(1− p)^(N−1)−k < k >= p(N −1) ^{p= < k>}

(N−1)

For large N and small k, we can use the following approximations:

N−1 k



 

 = (N−1)!

k!(N−1− k)!= (N−1)(N−1−1)(N−1−2)...(N −1− k+1)(N−1− k)!

k!(N −1− k)! = (N−1)^k k!

ln[(1− p)^(N−1)−k]=(N −1−k) ln(1− < k >

N −1) = −(N −1− k)< k >

N −1 = − < k >(1− k

N −1)≅ − < k >

(1 − p )

= e

P(k) = N−1 k



 

p^k(1− p)^(N−1)−k = (N −1)^k

k! p^ke^−<k> = (N −1)^k

k!

< k >

N−1



 



k

e^−<k> =e^−<k> < k >^k k! ln 1

(

)

( )

n=1 n

∑

2 + x³

3 −... ^for

x ≤ 1

POISSON DEGREE DISTRIBUTION

Network Science: Random Graphs

P(k) = N −1 k



 

p^k(1− p)^(N−1)−k < k >= p(N −1) ^{p= < k>}

(N−1)

For large N and small k, we arrive to the Poisson distribution:

P( k) = e

< k >

k !

DEGREE DISTRIBUTION OF A RANDOM GRAPH

Network Science: Random Graphs

P(k)

k

P( k ) = e

< k >

k !

<k>=50

DEGREE DISTRIBUTION OF A RANDOM NETWORK

Exact Result

-binomial distribution-

Large N limit

-Poisson distribution-

Probability Distribution Function (PDF)

Real Networks are not Poisson

Section 12.4

Section 12.5 Maximum and minimum degree

k_max=1,185

<k>=1,000, N=10⁹

P k e k

( ) k

min !

k

k

k k

0

∑

= _{−〈 〉} 〈 〉

=

.

<k>=1,000, N=10⁹

k_min=816

NO OUTLIERS IN A RANDOM SOCIETY

Network Science: Random Graphs

The most connected individual has degree k_max~1,185 The least connected individual has degree k_min ~ 816

The probability to find an individual with degree k>2,000 is 10^-27. Hence the chance of finding an individual with 2,000 acquaintances is so tiny that such nodes are virtually inexistent in a random society.

a random society would consist of mainly average individuals, with everyone with roughly the same number of friends.

It would lack outliers, individuals that are either highly popular or recluse.

P(k) =e^−<k> < k >^k

k!

FACING REALITY: Degree distribution of real networks

P(k) = e^−<k> < k>^k

k!

The evolution of a random network

Section 13

<k>

EVOLUTION OF A RANDOM NETWORK

disconnected nodes NETWORK.

How does this transition happen?

<k

>=1 (Erdos and Renyi, 1959) EVOLUTION OF A RANDOM NETWORK

disconnected nodes NETWORK.

The fact that at least one link per node is necessary to have a giant component is not unexpected. Indeed, for a giant component to exist, each of its nodes must be linked to at least one other node.

It is somewhat unexpected, however that one link is sufficient for the emergence of a giant component.

It is equally interesting that the emergence of the giant cluster is not gradual, but follows what physicists call a second order phase transition at <k>=1.

Section 13.1

Section 13.1

<k>

EVOLUTION OF A RANDOM NETWORK

disconnected nodes NETWORK.

How does this transition happen?

Phase transitions in complex systems I: Magnetism

Phase transitions in complex systems I: liquids

Water Ice

CLUSTER SIZE DISTRIBUTION

p(s) = e^−<k>s(< k > s)^s−1 s!

Probability that a randomly selected node belongs to a cluster of size s:

Network Science: Random Graphs

At the critical point <k>=1

The distribution of cluster sizes at the critical point, displayed in

a log-log plot. The data represent an average over 1000 systems of sizes

The dashed line has a slope of

−τn = −2.5

Derivation in Newman, 2010

k ^s−1 =exp[(s−1) ln k ]

p(s) = s^s−1

s! e^{− k s+(s−1) ln k}

s!= 2π^{s s} e



 



s

p(s) ~ s^{−3 / 2}e^{−( k −1)s+(s−1) ln k}

p(s) ~ s^{−3 / 2}

I:

Subcritical

<k> < 1

III:

Supercritical

<k> > 1

IV:

Connected

<k> > ln N II:

Critical

<k> = 1

<k>=0.5 <k>=1 <k>=3 <k>=5

N=100

<k>

I:

Subcritical

<k> < 1 p < p_c=1/N

<k>

No giant component.

N-L isolated clusters, cluster size distribution is exponential The largest cluster is a tree, its size ~ ln N

p(s) ~ s^{−3 / 2}e^{−( k −1)s+(s−1) ln k}

II:

Critical

<k> = 1 p=p_c=1/N

<k>

Unique giant component: N_G~ N^2/3

contains a vanishing fraction of all nodes, N_G/N~N^-1/3 Small components are trees, GC has loops.

Cluster size distribution: p(s)~s^-3/2

A jump in the cluster size:

N=1,000 ln N~ 6.9; N^2/3~95

N=7 10⁹ ln N~ 22; N^2/3~3,659,250

<k>=3

<k>

Unique giant component: N_G~ (p-p_c)N GC has loops.

Cluster size distribution: exponential

III:

Supercritical

<k> > 1 p > p_c=1/N

p(s) ~ s^{−3 / 2}e^{−( k −1)s+(s−1) ln k}

IV:

Connected

<k> > ln N p > (ln N)/N

<k>=5

<k>

Only one cluster: N_G=N GC is dense.

Cluster size distribution: None

Network evolution in graph theory

A gr aph has a gi ven pr oper t y Q i f t he pr obabi li t y of havi n g Q ap- pr oaches 1 as N ∞. That i s, f or a gi ven z ei t h er alm ost ever y gr aph has t h e pr oper t y Q or alm ost n o gr aph h as i t . For exam ple, f or z less

p =< k > /( N − 1)

Real networks are supercritical

Section 13.2

Section 13.2

Small worlds

Section 13.3

Frigyes Karinthy, 1929 Stanley Milgram, 1967

Peter

Jane

Sarah Ralph

SIX DEGREES small worlds

SIX DEGREES 1929: Frigyes Kartinthy

Frigyes Karinthy (1887-1938)

Hungarian Writer Network Science: Random Graphs

“Look, Selma Lagerlöf just won the Nobel Prize for Literature, thus she is bound to know King Gustav of Sweden, after all he is the one who handed her the Prize, as required by tradition. King Gustav, to be sure, is a passionate tennis player, who always participates in international tournaments. He is known to have played Mr. Kehrling, whom he must therefore know for sure, and as it happens I myself know Mr. Kehrling quite well.”

"The worker knows the manager in the shop, who knows Ford;

Ford is on friendly terms with the general director of Hearst Publications, who last year became good friends with Arpad Pasztor, someone I not only know, but to the best of my knowledge a good friend of mine. So I could easily ask him to send a telegram via the general director telling Ford that he should talk to the manager and have the worker in the shop quickly hammer together a car for me, as I happen to need one."

1929: Minden másképpen van (Everything is Different) Láncszemek (Chains)

SIX DEGREES 1967: Stanley Milgram

Network Science: Random Graphs

HOW TO TAKE PART IN THIS STUDY

1. ADD YOUR NAME TO THE ROSTER AT THE BOTTOM OF THIS SHEET, so that the next person who receives this letter will know who it came from.

2. DETACH ONE POSTCARD. FILL IT AND RETURN IT TO HARVARD UNIVERSITY.

No stamp is needed. The postcard is very important. It allows us to keep track of the progress of the folder as it moves toward the target person.

3. IF YOU KNOW THE TARGET PERSON ON A PERSONAL BASIS, MAIL THIS FOLDER DIRECTLY TO HIM (HER). Do this only if you have previously met the target person and know each other on a first name basis.

4. IF YOU DO NOT KNOW THE TARGET PERSON ON A PERSONAL BASIS, DO NOT TRY TO CONTACT HIM DIRECTLY. INSTEAD, MAIL THIS FOLDER (POST

CARDS AND ALL) TO A PERSONAL ACQUAINTANCE WHO IS MORE LIKELY THAN YOU TO KNOW THE TARGET PERSON. You may send the folder to a friend, relative or acquaintance, but it must be someone you know on a first name basis.

SIX DEGREES 1967: Stanley Milgram

Network Science: Random Graphs

SIX DEGREES 1991: John Guare

Network Science: Random Graphs

"Everybody on this planet is separated by only six other people.

Six degrees of separation. Between us and everybody else on this planet. The president of the United States. A gondolier in Venice…. It's not just the big names. It's anyone. A native in a rain forest. A Tierra del Fuegan. An Eskimo. I am bound to everyone on this planet by a trail of six people. It's a profound thought. How every person is a new door, opening up into other worlds."

WWW: 19 DEGREES OF SEPARATION

Image by Matthew Hurst

Blogosphere Network Science: Random Graphs

DISTANCES IN RANDOM GRAPHS

Random graphs tend to have a tree-like topology with almost constant node degrees.

Network Science: Random Graphs

d

= log N log k N = 1 + k + k

+ ... + k

= k

− 1

k − 1 ≈ k

DISTANCES IN RANDOM GRAPHS

Network Science: Random Graphs

d

= log N log k

< d >= log N log k

We will call the small world phenomena the property that the average path length or the diameter depends logarithmically on the system size.

Hence, ”small” means that ⟨d⟩ is proportional to log N, rather than N.

In most networks this offers a better approximation to the average distance between two randomly chosen nodes, ⟨d⟩, than to d_max .

The 1/log⟨k⟩ term implies that denser the network, the smaller will be the distance between the nodes.

Network Science: Graph Theory

Average Degree

Given the huge differences in scope, size, and average degree, the agreement is excellent.

DISTANCES IN RANDOM GRAPHS compare with real data

Why are small worlds surprising? Suprising compared to what?

Network Science: Random Graphs

Three, Four or Six Degrees?

For the globe ’ s social networks:

⟨k⟩ ≃ 10

N ≃ 7 × 10

for the world ’ s population.

< d >= ln(N )

ln k = 3.28

Image by Matthew Hurst Blogosphere

Clustering coefficient

Section 9

Since edges are independent and have the same probability p,

< L_i >≅ pk_i(k_i −1) 2

•The clustering coefficient of random graphs is small.

•For fixed degree C decreases with the system size N.

•C is independent of a node’s degree k.

C

≡ 2 < L

>

k

( k

− 1)

CLUSTERING COEFFICIENT

C decreases with the system size N.

C is independent of a node’s degree k.

Network Science: Random Graphs

CLUSTERING COEFFICIENT

Image by Matthew Hurst Blogosphere

Watts-Strogatz Model

Real networks are not random

Section 10

As quantitative data about real networks became available, we can compare their topology with the predictions of random graph theory.

Note that once we have N and <k> for a random network, from it we can derive every measurable property. Indeed, we have:

Average path length:

Clustering Coefficient:

Degree Distribution:

< l_rand >≈ logN log k

ARE REAL NETWORKS LIKE RANDOM GRAPHS?

Network Science: Random Graphs

P(k) =e^−<k> < k>^k

k!

Real networks have short distances like random graphs.

Prediction:

PATH LENGTHS IN REAL NETWORKS

Network Science: Random Graphs

< d >= log N

log k

Prediction:

C

underestimates with orders of magnitudes the clustering coefficient of real networks.

CLUSTERING COEFFICIENT

Network Science: Random Graphs

P(k) ≈ k^−γ

Prediction:

Data:

THE DEGREE DISTRIBUTION

Network Science: Random Graphs

P(k) =e^−<k> < k>^k

k!

As quantitative data about real networks became available, we can compare their topology with the predictions of random graph theory.

Note that once we have N and <k> for a random network, from it we can derive every measurable property. Indeed, we have:

Average path length:

Clustering Coefficient:

Degree Distribution:

< l_rand >≈ logN log k

ARE REAL NETWORKS LIKE RANDOM GRAPHS?

Network Science: Random Graphs

P(k) =e^−<k> < k>^k

k!

(B) Most important: we need to ask ourselves, are real networks random?

The answer is simply: NO

There is no network in nature that we know of that would be described by the random network model.

IS THE RANDOM GRAPH MODEL RELEVANT TO REAL SYSTEMS?

Network Science: Random Graphs

It is the reference model for the rest of the class.

It will help us calculate many quantities, that can then be compared to the real data, understanding to what degree is a particular property the result of some random process.

Patterns in real networks that are shared by a large number of real networks, yet which deviate from the predictions of the random network model.

In order to identify these, we need to understand how would a particular property look like if it is driven entirely by random processes.

While WRONG and IRRELEVANT, it will turn out to be extremly USEFUL!

IF IT IS WRONG AND IRRELEVANT, WHY DID WE DEVOT TO IT A FULL CLASS?

Network Science: Random Graphs

Scale-free property

Section 1

Nodes: WWW documents Links: URL links

Over 3 billion documents ROBOT: collects all URL’s found in a document and follows them recursively

WORLD WIDE WEB

R. Albert, H. Jeong, A-L Barabasi, Nature, 401 130 (1999).

Power laws and scale-free networks

Section 2

Nodes: WWW documents Links: URL links

Over 3 billion documents ROBOT: collects all URL’s found in a document and follows them recursively

R. Albert, H. Jeong, A-L Barabasi, Nature, 401 130 (1999).

WORLD WIDE WEB

Network Science: Scale-Free Property

Discrete vs. Continuum formalism

Network Science: Scale-Free Property

Discrete Formalism

As node degrees are always positive integers, the discrete formalism captures the probability that a node has exactly k links:

Continuum Formalism

In analytical calculations it is often convenient to assume that the degrees can take up any positive real value:

INTERPRETATION:

80/20 RULE

Vilfredo Federico Damaso Pareto (1848 – 1923), Italian economist, political scientist and philosopher, who had important contributions to our understanding of income distribution and to the analysis of individuals choices.

A number of fundamental principles are named after him, like Pareto efficiency, Pareto distribution (another name for a power-law distribution), the Pareto principle (or 80/20 law).

Hubs

Section 3

The difference between a power law and an exponential distribution

The difference between a power law and an exponential distribution

Let us use the WWW to illustrate the properties of the high-k regime.

The probability to have a node with k~100 is

•About in a Poisson distribution

•About if p_k follows a power law.

•Consequently, if the WWW were to be a random network, according to the Poisson prediction we would expect 10^-18 k>100 degree nodes, or none.

•For a power law degree distribution, we expect about k>100 degree nodes

Network Science: Scale-Free Property

Finite scale-free networks

All real networks are finite let us explore its consequences.

We have an expected maximum degree, k_max Estimating k_max

P(k)dk

k_max

∞

∫

k_max = k_minN

1 γ−1

Why: the probability to have a node larger than k_maxshould not exceed the prob. to have one node, i.e. 1/N fraction of all

nodes

P(k)dk

k_max

∞

∫

^γ

k_max

∞

∫

^γ _γ

∞ = k_min^γ−1

k_max^γ−1 ≈ 1 N

The size of the biggest hub

Finite scale-free networks

k

= k

N

1 γ −1

The size of the biggest hub

Finite scale-free networks

Expected maximum degree, k_max

k_max = k_minN

1 γ−1

•k_max, increases with the size of the network

the larger a system is, the larger its biggest hub

•For γ>2 k_maxincreases slower than N

the largest hub will contain a decreasing fraction of links as N increases.

•For γ=2 k_max~N.

The size of the biggest hub is O(N)

•For γ<2 k_maxincreases faster than N: condensation phenomena

the largest hub will grab an increasing fraction of links. Anomaly!

Finite scale-free networks

k_max = k_minN

1 γ−1

The size of the largest hub

The meaning of scale-free

Section 4

Definition:

Networks with a power law tail in their degree distribution are called

‘scale-free networks’

Where does the name come from?

Critical Phenomena and scale-invariance (a detour)

Slides after Dante R. Chialvo

Scale-free networks: Definition

Network Science: Scale-Free Property

Phase transitions in complex systems I: Magnetism

T = 0.99 T_c T = 0.999 T_c

ξ ξ

T = T_c T = 1.5 T_c T = 2 T_c

Network Science: Scale-Free Property

At T = Tc:

correlation length diverges

Fluctuations emerge at all scales:

scale-free behavior

Scale-free behavior in space

Network Science: Scale-Free Property

• Correlation length diverges at the critical point: the whole system is correlated!

• Scale invariance: there is no characteristic scale for the fluctuation (scale-free behavior).

• Universality: exponents are independent of the system’s details.

CRITICAL PHENOMENA

Network Science: Scale-Free Property

C = 1 k^−γ dk

k_min

∞

∫

= (

γ

k_min

∞

∫

P(k) = (

γ

Divergences in scale-free distributions

Network Science: Scale-Free Property

< k^m >= k^mP(k)dk

k_min

∞

∫

^γ

k_min

∞

∫

^γ

_γ

∞

If m-γ+1<0: < k^m >= − (

γ

(m−

γ

If m-γ+1>0, the integral diverges.

For a fixed γ this means that all moments with m>γ-1 diverge.

< k^m >= (

γ

k_min

∞

∫

^γ

_γ

∞

For a fixed λ this means all moments m>γ-1 diverge.

Many degree exponents are smaller than 3

<k²> diverges in the N ∞ limit!!!

DIVERGENCE OF THE HIGHER MOMENTS

Network Science: Scale-Free Property

The meaning of scale-free

The meaning of scale-free

universality

Section 5

(Faloutsos, Faloutsos and Faloutsos, 1999)

Nodes: computers, routers Links: physical lines

INTERNET BACKBONE

Network Science: Scale-Free Property

Network Science: Scale-Free Property