233-234

85
233-234 233-234 Sedgewick & Wayne (2004); Sedgewick & Wayne (2004); Chazelle (2005) Chazelle (2005)

Upload: easter

Post on 05-Jan-2016

22 views

Category:

Documents


0 download

DESCRIPTION

233-234. Sedgewick & Wayne (2004); Chazelle (2005). Adjacency lists. Hansel & Gretel. random walk. 1. Birds eat the bread crumbs. DFS/BFS. 2. They don’t. Diffusion equation. Random walk. Diffusion equation. Normal distribution. 3 views of the same thing. Hansel & Gretel. - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: 233-234

233-234233-234233-234233-234

Sedgewick & Wayne (2004); Chazelle (2005)Sedgewick & Wayne (2004); Chazelle (2005) Sedgewick & Wayne (2004); Chazelle (2005)Sedgewick & Wayne (2004); Chazelle (2005)

Page 2: 233-234

Adjacency lists

Page 3: 233-234

1. Birds eat the bread crumbs

2. They don’t

random walk

DFS/BFS

Hansel & Gretel

Page 4: 233-234
Page 5: 233-234

Diffusion equation

Page 6: 233-234

Diffusion equation

Normal distribution

Random walk

Page 7: 233-234
Page 8: 233-234

With bread crumbs one canfind exit in time proportional

to V+E DFS/BFS

Hansel & Gretel

Page 9: 233-234

Breadth First Search

Page 10: 233-234
Page 11: 233-234
Page 12: 233-234
Page 13: 233-234
Page 14: 233-234
Page 15: 233-234

Breadth First Search

F

A

B C G

D E

H

Page 16: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: A

get

0distance from A

visit(A)

Page 17: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue:

0

F1

F discovered

Page 18: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: F

0

1

B 1B discovered

Page 19: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: F B

0

1

1 C 1C discovered

Page 20: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: F B C

0

1

1 1 G

1

G discovered

Page 21: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: F B C G

get

0

1

1 1

1

A finished

Page 22: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: B C G

0

1

1 1

1A already

visited

Page 23: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: B C G

0

1

1 1

1

D2

D discovered

Page 24: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: B C G D

0

1

1 1

1

2 E

2

E discovered

Page 25: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: B C G D E

get

0

1

1 1

1

2

2

F finished

Page 26: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: C G D E

0

1

1 1

1

2

2

Page 27: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: C G D E

0

1

1 1

1

2

2

A alreadyvisited

Page 28: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: C G D E

get

0

1

1 1

1

2

2

B finished

Page 29: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: G D E

0

1

1 1

1

2

2

A alreadyvisited

Page 30: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: G D E

get

0

1

1 1

1

2

2

C finished

Page 31: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: D E

0

1

1 1

1

2

2

A alreadyvisited

Page 32: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: D E

0

1

1 1

1

2

2

E alreadyvisited

Page 33: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: D E

get

0

1

1 1

1

2

2

G finished

Page 34: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: E

0

1

1 1

1

2

2

E alreadyvisited

Page 35: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: E

0

1

1 1

1

2

2

F alreadyvisited

Page 36: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue: E

get

0

1

1 1

1

2

2

D finished

Page 37: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue:

0

1

1 1

1

2

2

D alreadyvisited

Page 38: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue:

0

1

1 1

1

2

2

F alreadyvisited

Page 39: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue:

0

1

1 1

1

2

2

G alreadyvisited

Page 40: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue:

0

1

1 1

1

2

2

H 3

H discovered

Page 41: 233-234

Breadth First Search

F

A

B C G

D E

Queue: H

get

0

1

1 1

1

2

2

H 3

E finished

Page 42: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue:

0

1

1 1

1

2

2

3

E alreadyvisited

Page 43: 233-234

Breadth First Search

F

A

B C G

D E

H

Queue:

STOP

0

1

1 1

1

2

2

3

H finished

Page 44: 233-234

Breadth First Search

F

A

B C G

D E

H

0

1

1 1

1

2

2

3

distance from A

Page 45: 233-234

Breadth-First Search

Page 46: 233-234

b ca da

cdb

v

Page 47: 233-234
Page 48: 233-234
Page 49: 233-234
Page 50: 233-234

Rod Steiger

Martin Sheen

Donald Pleasence

#1

#2

#3

#876Kevin Bacon

Barabasi

Page 51: 233-234

Rank NameAveragedistance

# ofmovies

# oflinks

1 Rod Steiger 2.537527 112 25622 Donald Pleasence 2.542376 180 28743 Martin Sheen 2.551210 136 35014 Christopher Lee 2.552497 201 29935 Robert Mitchum 2.557181 136 29056 Charlton Heston 2.566284 104 25527 Eddie Albert 2.567036 112 33338 Robert Vaughn 2.570193 126 27619 Donald Sutherland 2.577880 107 2865

10 John Gielgud 2.578980 122 294211 Anthony Quinn 2.579750 146 297812 James Earl Jones 2.584440 112 3787…

876 Kevin Bacon 2.786981 46 1811…

Why Kevin Bacon?

Measure the average distance between Kevin Bacon and all other actors.

876 Kevin Bacon 2.786981 46 1811Barabasi

Page 52: 233-234
Page 53: 233-234

Langston et al., A combinatorial approach to the analysis of differential gene expression data….

Minimum Dominating Set

Page 54: 233-234

Minimum Dominating Set

Page 55: 233-234

Minimum Dominating Set

Page 56: 233-234

size of dominating set

Page 57: 233-234

Expected size of dominating set

Assume each node has at least d neighbors

Naïve algorithm still n/2 in worst case

Simple probabilistic algorithm:

Page 58: 233-234

1. For each vertex v, color v red with probability p

Page 59: 233-234

1. For each vertex v, color v red with probability p

2. Color blue any non-dominated vertex

Page 60: 233-234

X= number of red nodes Y= number of blue nodes

Size of dominating set = X+Y

Page 61: 233-234
Page 62: 233-234
Page 63: 233-234
Page 64: 233-234
Page 65: 233-234

Expected size of dominating set S =

Page 66: 233-234

Markov’s inequality

proof

j= k E|S|

Page 67: 233-234

Probability that is < 1/2

Run algorithm 10 times and keep smallest S

with probability > 0.999

Page 68: 233-234

protein-protein

interactions

PROTEOME

GENOME

Citrate Cycle

METABOLISM

Bio-chemical reactions

Barabasi

Page 69: 233-234

Tucker-Gera-Uetz

Page 70: 233-234
Page 71: 233-234

Local network motifs

SIM MIM FFLFBL

[Alon; Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]

Page 72: 233-234

Barabasi

Page 73: 233-234

The New Science of Networks by Barabasi

Page 74: 233-234

Degree DistributionDegree Distribution

PP((kk) = probability a given node has ) = probability a given node has exactly exactly kk neighbors neighbors

Random NetworkRandom Network P(k) = PoissonP(k) = Poisson ~~ No hubsNo hubs

Scale free NetworkScale free Network P(k) ~P(k) ~ . .

A few hubsA few hubs

Page 75: 233-234

Metabolic network

Organisms from all three domains of life are scale-free networks!

H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature, 407 651 (2000)

Archaea Bacteria Eukaryotes

Page 76: 233-234

Barabasi & Albert, Science 286, 509 (1999)

Actors

Movies

Web-pages

Hyper-links

Trans. stations

Power lines

Nodes:

Links:

Scale-free networksScale-free networks

Page 77: 233-234

Why scale-free topology in biological Why scale-free topology in biological networks ?networks ?

Page 78: 233-234

Preferential attachment

Page 79: 233-234
Page 80: 233-234

Mean Field Theory

γ = 3

t

k

k

kAk

t

k i

j j

ii

i

2)(

ii t

tmtk )(

, with initial condition mtk ii )(

)(1)(1)())((

02

2

2

2

2

2

tmk

tm

k

tmtP

k

tmtPktkP ititi

33

2

~12))((

)(

kktm

tm

k

ktkPkP

o

i

A.-L.Barabási, R. Albert and H. Jeong, Physica A 272, 173 (1999)

Page 81: 233-234

Clustering in protein interaction networks

Goldberg and Roth, PNAS, 2003

high clustering = high quality of interaction

|))(||,)(min(|

|)()(| |)(||)(|

|)(||)(|log

wNvN

wNvNi

vwwN

N

iwN

vNN

i

vNC

Page 82: 233-234

Scale-free model(1) GROWTH : At every timestep we add a new node with m edges (connected to the nodes already present in the system).

(2) PREFERENTIAL ATTACHMENT : The probability Π that a new node will be connected to node i depends on the connectivity ki of that node

A.-L.Barabási, R. Albert, Science 286, 509 (1999)

jj

ii k

kk

)(

P(k) ~k-3

Page 83: 233-234

Why scale-free topology in biological Why scale-free topology in biological networks ?networks ?

Page 84: 233-234
Page 85: 233-234

Yeast protein networkNodes: proteins

Links: physical interactions (binding)

P. Uetz, et al. Nature, 2000; Ito et al., PNAS, 2001; …