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Networks and Scaling

Networks and Scaling

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Networks and Scaling. Distributions and Scaling . What is a numerical distribution ? What is scaling ?. Example: Human height follows a normal distribution. Frequency. Height. http://scienceblogs.com/builtonfacts/2009/02/the_central_limit_theorem_made.php. - PowerPoint PPT Presentation

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Page 1: Networks and Scaling

Networks and Scaling

Page 2: Networks and Scaling

Distributions and Scaling

• What is a numerical distribution?

• What is scaling?

Page 3: Networks and Scaling

http://scienceblogs.com/builtonfacts/2009/02/the_central_limit_theorem_made.php

Example: Human height follows a normal distribution

Height

Frequency

Page 4: Networks and Scaling

Example: Population of cities follows a power-law (“scale-free) distribution

http://upload.wikimedia.org/wikipedia/commons/4/49/Powercitiesrp.png

http://www.streetsblog.org/wp-content/uploads 2006/09/350px_US_Metro_popultion_graph.png

http://cheapukferries.files.wordpress.com/2010/06/hollandcitypopulation1.png

Page 5: Networks and Scaling

part of WWW

Degree

Num

ber o

f nod

es

Degree

Num

ber o

f nod

es

Page 6: Networks and Scaling

Degree

“Scale-free” distribution

21

k kdegreewithnodesofNumber

The Web’s approximate Degree Distribution

Num

ber o

f nod

es

“power law”

Page 7: Networks and Scaling

Degree

“Scale-free” distribution

21

k kdegreewithnodesofNumber

The Web’s approximate Degree Distribution

Num

ber o

f nod

es

“power law”

Page 8: Networks and Scaling

k ≈1k 2

log

(Num

ber o

f nod

es)

Num

ber o

f nod

es

Degree k log (Degree)

A power law, plotted on a “log-log” plot, is a straight line.

The slope of the line is the exponent of the power law.

From http://www.pnas.org/content/105/37/13724/F4.expansion.html

logk ≈ log1k 2

⎛ ⎝ ⎜

⎞ ⎠ ⎟= log k −2( ) = −2logk

Page 9: Networks and Scaling

Other examples of power laws in nature

Gutenberg-Richter law of earthquake magnitudes

By: Bak [1]

Page 10: Networks and Scaling

Metabolic scaling in animals

Page 11: Networks and Scaling

Rank-frequency scaling: Word frequency in English(Zipf’s law)

A plot of word frequency of single words (unigrams) versus rank r extracted from the one million words of the Brown’s English dictionary. (http://web.me.com/kristofferrypdal/Themes_Site/Scale_invariance.html)

Page 12: Networks and Scaling

http://cs.pervasive.com/blogs/datarush/Figure2.png

Page 13: Networks and Scaling
Page 14: Networks and Scaling

Rank-frequency scaling: City populations

http://brenocon.com/blog/2009/05/zipfs-law-and-world-city-populations/

Page 15: Networks and Scaling

Rank-frequency scaling: Income distribution

Page 16: Networks and Scaling

From A Unified Theory of Urban Living, L. Bettencourt and G. West, Nature, 467, 912–913, 2010

Scaling in cities

Page 17: Networks and Scaling

http://mjperry.blogspot.com/2008/08/more-on-medal-inequality-at-2008.html

Page 18: Networks and Scaling

What causes these distributions?

Page 19: Networks and Scaling

Interesting distribution: “Benford’s law”

Page 20: Networks and Scaling

In-class exercise: Benford’s Law

• City populationshttp://www.census.gov/population/www/documentation/twps0027/tab22.txt

Page 21: Networks and Scaling

Benford’s law: Distribution of leading digits

Newcomb’s observation

Explanation of Benford’s law?

http://www.youtube.com/watch?v=O8N26edbqLM

Page 22: Networks and Scaling

Collect distribution of leading digits in corporate accounting statements of total assets

Plot deviations from Benford’s law versus year

http://econerdfood.blogspot.com/2011/10/benfords-law-and-decreasing-reliability.html

Page 23: Networks and Scaling

“Bernie vs Benford’s Law: Madoff Wasn’t That Dumb”

http://paul.kedrosky.com/archives/2008/12/bernie_vs_benfo.html

Frequency of leading digits in returns reported by Bernie Madoff’s funds

Page 24: Networks and Scaling

Controversy: Can Network Structure and Dynamics

Explain Scaling in Biology and Other Disciplines?

Scaling: How do properties of systems (organisms,

economies, cities) change as their size is varied?

Example: How does basal metabolic rate (heat radiation)

vary as a function of an animal’s body mass?

Page 25: Networks and Scaling

Metabolic scaling

• Surface hypothesis: – Body is made of cells, in which metabolic reactions take

place. – Can “approximate” body mass by a sphere of cells with

radius r. – Can approximate metabolic rate by surface area

r

Page 26: Networks and Scaling

Mouse

Hamster

Hippo

Page 27: Networks and Scaling

Mouse

HamsterRadius = 2 Mouse radius

HippoRadius = 50 Mouse radius

Page 28: Networks and Scaling

Mouse

HamsterRadius = 2 Mouse radius

HippoRadius = 50 Mouse radiusHypothesis 1: metabolic rate body

mass

Page 29: Networks and Scaling

Problem: Mass is proportional to volume of animalbut heat can radiate only from surface of animal

Mouse

HamsterRadius = 2 Mouse radius

Hypothesis 1: metabolic rate body mass

HippoRadius = 50 Mouse radius

Page 30: Networks and Scaling

Problem: mass is proportional to volume of animalbut heat can radiate only from surface of animal

Mouse

HamsterRadius = 2 Mouse radius

Hypothesis 1: metabolic rate body mass

HippoRadius = 50 Mouse radius

Volume of a sphere:

Surface area of a sphere:

3

34 r

24 r

Page 31: Networks and Scaling

Problem: mass is proportional to volume of animalbut heat can radiate only from surface of animal

Mouse

Hypothesis 1: metabolic rate body mass

HippoRadius = 50 Mouse radius

Volume of a sphere:

Surface area of a sphere:

3

34 r

24 r

HamsterRadius = 2 Mouse radiusMass 8 Mouse radiusSurface area 4 Mouse radius

Page 32: Networks and Scaling

Problem: mass is proportional to volume of animalbut heat can radiate only from surface of animal

Mouse

Hypothesis 1: metabolic rate body mass

Volume of a sphere:

Surface area of a sphere:

3

34 r

24 r

HamsterRadius = 2 Mouse radiusMass 8 Mouse radiusSurface area 4 Mouse radius

HippoRadius = 50 Mouse radiusMass 125,000 Mouse radiusSurface area 2,500 Mouse radius

Page 33: Networks and Scaling

Volume of a sphere:

Surface area of a sphere:

Surface area scales with volume to the 2/3 power.

3

34 r

24 r

“Volume of a sphere scales as the radius cubed”

“Surface area of a sphere scales as the radius squared”

mouse

hamster(8 mouse mass) hippo

(125,000 mouse mass)

Page 34: Networks and Scaling

Volume of a sphere:

Surface area of a sphere:

Surface area scales with volume to the 2/3 power.

3

34 r

24 r

“Volume of a sphere scales as the radius cubed”

“Surface area of a sphere scales as the radius squared”

mouse

hamster(8 mouse mass) hippo

(125,000 mouse mass)

Hypothesis 2 (“Surface Hypothesis): metabolic rate mass2/3

Page 35: Networks and Scaling

y = x2/3

log (body mass)

log (metabolic rate)

Page 36: Networks and Scaling

Actual data: y = x3/4

Page 37: Networks and Scaling

Actual data:

Hypothesis 3 (“Keiber’s law): metabolic rate mass3/4

y = x3/4

Page 38: Networks and Scaling

Actual data:

For sixty years, no explanation

Hypothesis 3 (“Keiber’s law): metabolic rate mass3/4

y = x3/4

Page 39: Networks and Scaling

Kleiber’s law extended over 21 orders of magnitude

Page 40: Networks and Scaling

y = x 3/4

y = x 2/3

metabolicrate

body mass

More “efficient”, in sense that metabolic rate (and thus rate of distribution of nutrients to cells) is larger than surface area would predict.

Page 41: Networks and Scaling

Other Observed Biological Scaling Laws

Heart rate body mass1/4

Blood circulation time body mass1/4

Life span body mass1/4

Growth rate body mass1/4

Heights of trees tree mass1/4

Sap circulation time in trees tree mass1/4

Page 42: Networks and Scaling

West, Brown, and Enquist’s Theory(1990s)

Page 43: Networks and Scaling

West, Brown, and Enquist’s Theory(1990s)

General idea: “metabolic scaling rates (and other biological rates) are limited not by surface area but by rates at which energy and materials can be distributed between surfaces where they are exchanged and the tissues where they are used. “

How are energy and materials distributed?

Page 44: Networks and Scaling

Distribution systems

Page 45: Networks and Scaling
Page 46: Networks and Scaling

West, Brown, and Enquist’s Theory(1990s)

• Assumptions about distribution network:– branches to reach all parts of three-dimensional organism(i.e., needs to be as “space-filling” as possible)

– has terminal units (e.g., capillaries) that do not vary with size among organisms

– evolved to minimize total energy required to distribution resources

Page 47: Networks and Scaling
Page 48: Networks and Scaling
Page 49: Networks and Scaling

• Prediction: Distribution network will have fractal branching structure, and will be similar in all / most organisms (i.e., evolution did not optimize distribution networks of each species independently)

• Therefore, Euclidean geometry is the wrong way to view scaling; one should use fractal geometry instead!

• With detailed mathematical model using three assumptions, they derive

metabolic rate body mass3/4

Page 50: Networks and Scaling

Their interpretation of their model• Metabolic rate scales with body mass like surface area scales

with volume...

but in four dimensions.

Page 51: Networks and Scaling

• “Although living things occupy a three-dimensional space, their internal physiology and anatomy operate as if they were four-dimensional. . . Fractal geometry has literally given life an added dimension.”― West, Brown, and Enquist

Page 52: Networks and Scaling

Critiques of their model

• E.g.,

• Bottom line: Model is interesting and elegant, but both the explanation and the underlying data are controversial.

• Validity of these ideas beyond biology?

Page 53: Networks and Scaling

Do fractal distribution networks explainscaling in cities?

Cf. Bettencourt, Lobo, Helbing, Kuhnert, and West, PNAS 2007

“[L]ife at all scales is sustained by optimized, space-filling, hierarchical branching networks, which grow with the size of the organism as uniquely specified approximately self-similar structures.”

Page 54: Networks and Scaling

Total wages per metropolitan area vs. population

Page 55: Networks and Scaling

Walking speed vs. population

Page 56: Networks and Scaling

“Supercreative” employment vs. population