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Classes will begin shortly. Networks, Complexity and Economic Development. Characterizing the Structure of Networks Cesar A. Hidalgo PhD. WWW. Expected. P( k ) ~ k - . Found. Over 3 billion documents. Exponential Network. Scale-free Network. - PowerPoint PPT PresentationTRANSCRIPT
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Networks, Complexity and Economic Development
Characterizing the Structure of Networks
Cesar A. Hidalgo PhD
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Over 3 billion documents
R. Albert, H. Jeong, A-L Barabasi, Nature, 401 130 (1999).
Expected
P(k) ~ k-
FoundSca
le-f
ree N
etw
ork
Exponenti
al N
etw
ork
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Take home messages
-Networks might look messy, but are not random.-Many networks in nature are Scale-Free (SF), meaning that just a few nodes have a disproportionately large number of connections.-Power-law distributions are ubiquitous in nature.-While power-laws are associated with critical points in nature, systems can self-organize to this critical state.- There are important dynamical implications of the Scale-Free topology.-SF Networks are more robust to failures, yet are more vulnerable to targeted attacks.-SF Networks have a vanishing epidemic threshold.
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Local Measures
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CENTRALITY MEASURES
Measure the “importance”of a node in a network.
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8
Rod Steiger
Click on a name to see that person's table. Steiger, Rod (2.678695) Lee, Christopher (I) (2.684104) Hopper, Dennis (2.698471) Sutherland, Donald (I) (2.701850) Keitel, Harvey (2.705573) Pleasence, Donald (2.707490) von Sydow, Max (2.708420) Caine, Michael (I) (2.720621) Sheen, Martin (2.721361) Quinn, Anthony (2.722720) Heston, Charlton (2.722904) Hackman, Gene (2.725215) Connery, Sean (2.730801) Stanton, Harry Dean (2.737575) Welles, Orson (2.744593) Mitchum, Robert (2.745206) Gould, Elliott (2.746082) Plummer, Christopher (I) (2.746427) Coburn, James (2.746822) Borgnine, Ernest (2.747229)
Hollywood Revolves Around
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XXX
Most Connected Actors in Hollywood(measured in the late 90’s)
A-L Barabasi, “Linked”, 2002
Mel Blanc 759Tom Byron 679
Marc Wallice 535Ron Jeremy 500Peter North 491
TT Boy 449Tom London 436Randy West 425
Mike Horner 418Joey Silvera 410
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DEGREE CENTRALITY
K= number of links
Where Aij = 1 if nodes i and j are connected and 0 otherwise
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BETWENNESS CENTRALITY
BC= number of shortestPaths that go through a
node.
A B H
I
J
K
DG
E
C
F
BC(G)=0
N=11
BC(D)=9+1/2=9.5
BC(B)=4*6=24
BC(A)=5*5+4=29
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CLOSENESS CENTRALITY
C= Average Distanceto neighbors
A B H
I
J
K
DG
E
C
F
N=11
C(G)=1/10(1+2*3+2*3+4+3*5)C(G)=3.2
C(A)=1/10(4+2*3+3*3)C(A)=1.9
C(B)=1/10(2+2*6+2*3)C(B)=2
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EIGENVECTOR CENTRALITY
Consider the Adjacency Matrix Aij = 1 if node i is connected to node jand 0 otherwise.
Consider the eigenvalue problem:
Ax=x
Then the eigenvector centrality of a node is defined as:
where is the largest eigenvalue associated with A.
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PAGE RANK
PR=Probability that a randomwalker with interspersedJumps would visit that node.PR=Each page votes forits neighbors.
A E F
G
H
I
BK
C
J
D
PR(A)=PR(B)/4 + PR(C)/3 + PR(D)+PR(E)/2A random surfer eventually stops clicking
PR(X)=(1-d)/N + d(PR(y)/k(y))
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PAGE RANK
PR=Probability that a randomWalker would visit that node.PR=Each page votes forits neighbors.
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CLUSTERING MEASURES
Measure the densityof a group of nodes in a
Network
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Clustering Coefficient
A B H
I
J
K
DG
E
C
F
Ci=2/k(k-1)
CA=2/12=1/6 CC=2/2=1 CE=4/6=2/3
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Topological OverlapMutual Clustering
A B H
I
J
K
DG
E
C
F
TO(A,B)=Overlap(A,B)/NormalizingFactor(A,B)TO(A,B)=N(A,B)/max(k(A),k(B))
TO(A,B)=N(A,B)/ (k(A)xk(B))1/2
TO(A,B)=N(A,B)/min(k(A),k(B))
TO(A,B)=N(A,B)/(k(A)+k(B))
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Topological OverlapMutual Clustering
A B H
I
J
K
DG
E
C
F
TO(A,B)=N(A,B)/max(k(A),k(B))
TO(A,B)=0TO(A,D)=1/4TO(E,D)=2/4
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MOTIFS
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Motifs
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Structural Equivalence
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Global Measures
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The Distribution of any of the previously introduced measures
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Giant ComponentComponents
S=NumberOfNodesInGiantComponent/TotalNumberOfNodes
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Diameter
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A B H
I
J
K
DG
E
C
F
Diameter=Maximum Distance Between Elements in a Set
Diameter=D(G,J)=D(C,J)=D(G,I)=…=5
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A B
DG
E
C
F
Average Path Length
A B C D E F G
ABCDEFG
1 2 1 1 1 2 3 2 2 2 3 1 1 3 2 1 2 1 2 2 3
D(1)=8D(2)=9D(3)=4
L=(8+2x9+3x4)/(8+9+4) L=1.8
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Degree CorrelationsAre Hubs Connected to Hubs?
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Phys. Rev. Lett. 87, 258701 (2001)
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Wait:are we comparing to the right thing
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Compared to what?Randomized Network
AB
HI
J K
DG
EC
F AB
HI
J K
DG
EC
F AB
HI
J K
DG
EC
F
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Internet
Randomized Network
Physica A 333, 529-540 (2004)
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Connectivity Pattern/Randomized Z-score Connectivity Pattern/Randomized
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Data
Models
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After Controlling for Randomized Network
Data
Models
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Fractal Networks
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Mandelbrot BB
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GeneratingKoch Curve
Measuring the Dimension of Koch Curve
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White Noise
Pink Noise
Brown Noise
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Attack Tolerance
Fractal Network
Non Fractal Network
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Take Home Messages-To characterize the structure of a Network we need many different measures-This measures allow us to differentiate between the different networks in natureToday we saw:Local MeasuresCentrality measures (degree, closeness, betweenness, eigenvector, page-rank)Clustering measures (Clustering, Topological Overlap or Mutual Clustering)MotifsGlobal Measures Degree Correlations, Correlation Profile.Hierarchical StructureFractal Structure
Connections Between Local and Global Measures