233-234
description
Transcript of 233-234
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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)
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Adjacency lists
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1. Birds eat the bread crumbs
2. They don’t
random walk
DFS/BFS
Hansel & Gretel
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Diffusion equation
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Diffusion equation
Normal distribution
Random walk
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With bread crumbs one canfind exit in time proportional
to V+E DFS/BFS
Hansel & Gretel
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Breadth First Search
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Breadth First Search
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Breadth-First Search
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b ca da
cdb
v
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Rod Steiger
Martin Sheen
Donald Pleasence
#1
#2
#3
#876Kevin Bacon
Barabasi
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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
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Langston et al., A combinatorial approach to the analysis of differential gene expression data….
Minimum Dominating Set
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Minimum Dominating Set
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Minimum Dominating Set
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size of dominating set
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Expected size of dominating set
Assume each node has at least d neighbors
Naïve algorithm still n/2 in worst case
Simple probabilistic algorithm:
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1. For each vertex v, color v red with probability p
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1. For each vertex v, color v red with probability p
2. Color blue any non-dominated vertex
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X= number of red nodes Y= number of blue nodes
Size of dominating set = X+Y
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Expected size of dominating set S =
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Markov’s inequality
proof
j= k E|S|
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Probability that is < 1/2
Run algorithm 10 times and keep smallest S
with probability > 0.999
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protein-protein
interactions
PROTEOME
GENOME
Citrate Cycle
METABOLISM
Bio-chemical reactions
Barabasi
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Tucker-Gera-Uetz
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Local network motifs
SIM MIM FFLFBL
[Alon; Horak, Luscombe et al (2002), Genes & Dev, 16: 3017 ]
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Barabasi
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The New Science of Networks by Barabasi
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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
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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
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Barabasi & Albert, Science 286, 509 (1999)
Actors
Movies
Web-pages
Hyper-links
Trans. stations
Power lines
Nodes:
Links:
Scale-free networksScale-free networks
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Why scale-free topology in biological Why scale-free topology in biological networks ?networks ?
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Preferential attachment
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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)
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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
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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
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Why scale-free topology in biological Why scale-free topology in biological networks ?networks ?
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Yeast protein networkNodes: proteins
Links: physical interactions (binding)
P. Uetz, et al. Nature, 2000; Ito et al., PNAS, 2001; …