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It’s a Small World After All. - The small world phenomenon. Please hold applause until the end of the presentation. Kim Dressel. Angie Heimkes. Eric Larson. Kyle Pinion. Jason Rebhahn. Kyle Pinion Introduction and Conclusion Jason Rebhahn

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slide1

It’s a Small World After All

- The small world phenomenon

Please hold applause until the end of the presentation.

Kim Dressel

Angie Heimkes

Eric Larson

Kyle Pinion

Jason Rebhahn

slide2

Kyle Pinion

Introduction and Conclusion

Jason Rebhahn

The research of the small world phenomenon by Stanley Milgram, Steven Strogatz, and Duncan Watts. Examples how this phenomenon can be applied to realistic situations, including the world wide web.

Eric Larson

Definitions and terms involved in the mathematics behind the small world phenomenon. Introduction to lattice representations, short and long range contacts, metrics, and phase j.

Angie Heimkes & Kim Dressel

Proof of the main theorem behind Jon Kleinberg’s model of the Small-World Network.

slide3

The Small-World Phenomenon

“The idea that even in a planet with billions of people, everyone is connected in a tight network.”

Also known as the Six Degrees of Separation

slide4

Stanley Milgram - social psychologist at Harvard University

  • First to study the Small-World Phenomenon
  • 1967 - Performed chain letter experiment from the Midwest to Boston
  • Averaged 6 transitions of the letter
  • Sparked a wide interest in the study of the Small-World Phenomenon
slide5

Steven Strogatz - mathematician at Cornell University

Duncan Watts - social scientist at Columbia University

In 1998, the two developed a more refined model to represent the Small-World Network.

slide6

Watts-Strogatz Model

Based on Regular and Random Networks

Regular Network: A given point is only directly linked to its four nearest neighbors.

Random Network: Each point has a connection to a more distant point.

Small-World Network: A given point has four local connections plus a distant connection.

slide7

Watts-Strogatz Model

Supported the idea that the Small-World Phenomenon is pervasive in a wide range of networks in nature and technology.

  • Six Degrees of Kevin Bacon
  • Neural networks in elegan worms
  • Power grid of the United States
slide8

Small-World Model

Interest has spread to many areas of study including:

  • Economics
  • Physics
  • Neurophysiology
  • Biochemistry
slide9

World-Wide Web

Estimated size of 800 million documents.

The Northern Light search engine covers the largest amount at 38% of the web.

Since the Small-World Network applies very well to the WWW, search engines could make use of it to make more efficient searches over a larger amount of the web.

slide10

Jon Kleinberg - Professor at Cornell University

Developed the Clever Algorithm for searching the web more efficiently.

Determined the Watts-Strogatz Model was insufficient to explain the algorithmic concepts of Milgram’s Small-World Phenomenon.

lattices
Lattices

Lattice Drawing

  • n x n grid
  • nodes represent individuals in a social network

Lattice Distance

short and long range contacts
ShortandLong-rangeContacts

Short-range -

For p > 0 the node u has a directed edge on every other node within lattice distance p.

p = 1 and q = 2

Long-range -

For and a directed edge is made using independent random trials

slide14

The Decentralized algorithm A

  • Determine the long-range contact(s)
  • Transfer the message to the node closest to the target node
slide15

Inverse rth-power distribution

The ith directed edge from u has endpoint v with probability proportional to [D(u,v)]-r

To obtain a probability distribution, this is divided by an appropriate normalizing constant.

p = 1 and q = 2

slide16

Performance Metric

Performance in this system is measured by the average number of steps it takes to get from the source to the target. This can be defined mathematically as the Expectation of X.

slide18

Kleinberg’s Theorem

The theorem behind the model states that there is a decentralized algorithm A and a constant c, independent of n, so that when r = 2 and

p = q = 1, the expected delivery time of A is at most

slide19

Kleinberg’s Theorem

First of all, we will find the upper and lower bounds for the probability that u chooses v as its long-range contact. The probability that u chooses v as a long-range contact is

given by:

slide20

To find the upper bound, we have:

*We get (2n-2) as an upper limit because we are dealing with a finite lattice structure and the furthest point from the message holder is

(n-1) + (n-1) = (2n-2).*

slide23

Phase j

For j > 0, phase j is defined as

  • In this picture,
  • Red is the target
  • Green is phase j = 0
  • Yellow is phase j = 1
  • Blue is phase j = 2
  • Black is the start of phase j = 3
slide24

Ball j

For j > 0, Ball j is defined as

  • In this picture,
  • Red is the target
  • Green is B0
  • Yellow and green make B1
  • Blue, yellow, and green make B2
  • Black, blue, yellow, and green make the start of B3
slide26

Mathematical Background

Geometric Series: Each term in the series is obtained from the preceding one by multiplying it by a common ratio.

Probability: It is used to mean the chance that a particular event will occur expressed on a linear scale 0 to 1.

= a/(1-r)

slide27

Mathematical Background

Discrete Random Variable: Assumes each of its values with a certain probability. Must be between 1 and 0 with the sum of 1

Logarithms: log n denotes the logarithm base 2, while ln n denotes the natural logarithm, base e

slide30

Proof of expectation

...By the law of total probability

slide33

So is it really a small world after all?

What if...

  • there were different values for p and q?
  • different formulas were used for probability and the decentralized algorithms?
  • the definitions for long and short range contacts changed?
  • metric spaces allowed diagonal movement, not just up & down?
slide34

Maybe the world is not as small as we think.

We’ll let you decide for yourself and come up with your own model.

slide35

Wrapping it up….

Thanks for coming!

Kim Dressel

Angie Heimkes

Eric Larson

Kyle Pinion

Jason Rebhahn

slide36

REFERENCES

1. L. Adamic, “The Small World Web” , manuscript available at http://www.parc.xerox.com/istl/groups/iea/www/smallworld.html

2. Sandra Blakeslee, “Mathematics Prove That It’s a Small World”

3. Dr. Steve Deckelman, His Extensive Mathematical Knowledge

4. Jon Kleinberg, “The Small-World Phenomenon: An Algorithmic Perspective”

5. Stanley Milgram, “The Small World Problem” Psychology Today 1, 61 (1967)

5. Beth Salnier, “Small World”

6. Reka Albert, Hawoong Jeong, Albert-Laszlo Barabasi, “Diameter of the World- Wide Web” Nature. 401, 130 (1999)

Thanks again Steve!