Eurecom, Sophia-Antipolis Thrasyvoulos Spyropoulos / [email protected] Part II: Complex Networks...
-
Upload
alvin-benson -
Category
Documents
-
view
213 -
download
1
Transcript of Eurecom, Sophia-Antipolis Thrasyvoulos Spyropoulos / [email protected] Part II: Complex Networks...
Eurecom, Sophia-AntipolisThrasyvoulos Spyropoulos / [email protected]
Part II: Complex Networks
Empirical Properties and Metrics
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Textbooks
2
“Networks, Crowds, and Markets: Reasoning About a Highly Connected World” by D. Easley and T. Kleinberg (“NCM”: publicly available online) · “Networks: An Introduction” by M. Newman – (“Networks”: shared copies in library)
Networked Life: 20 Questions and Answers by M.Chiang (some chapters - shared copies in library)
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
What is a Network?
A set of “nodes” Humans, routers, web pages, telephone switches, airports,
proteins, scientific articles …
Relations between these nodes humans: friendship/relation or online friendship routers, switches: connected by a communication link web pages: hyperlinks from one to other airports: direct flights between them articles: one citing the other proteins: link if chemically interacting
Network often represented asa graph: vertex = node link relation (weight strength)
3
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Social Networks (of the past)
4
The social network of friendships within a 34-person karate club provides clues to the fault lines that eventually split the club apart (Zachary, 1977)
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Social Networks (of the past)
5
High school dating
Peter S. Bearman, James Moodyand Katherine StovelChains of affection: The structure ofadolescent romantic and sexual networksAmerican Journal of Sociology 11044-91 (2004)Image drawn by Mark Newman
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Network Research of the Past
Mostly done by Social Scientists Interested in Human (Social) Networks Spread of Diseases, Influence, etc.
Methodology: Questionnaires cumbersome, (lots of) bias
Network Size: 10s or at most 100s
6
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Email Network
7
Email flows amongst a large project team. Colors denote each participant’s department
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
(a subset!) of the Internet Graph
9
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
The Science of Complex Networks
The study of large networks coming from all sorts of diverse areas We will focus on technological (e.g. Internet) and information networks (e.g.
Web, Facebook) Cannot visually observe such networks (as in the case of old social networks
of few 10s of nodes) need ways to measure them, and quantify their properties
The field is often called Social Networks or Network Science or Network Theory
Question 1: What are the statistical properties of real networks? Connectivity, paths lengths, degree distributions How do we measure such huge networks sampling
Question 2: Why do these properties arise? Models of large networks: random graphs Deterministic ways too complex/restrictive
Question 3: How can we take advantage of these properties? Connectivity (epidemiology, resilience) Spread (information, disease) Search (Web page, person)
10
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Part I: Network Properties of Interest
There are a lot of different properties we might be interested in also depends on application
But there are some commonly studied properties for 2 reasons:1. These properties are important for key applications2. The majority of networks exhibit surprising similarities with
respect to these properties.
1. Degree distribution (“scale free structure”)2. Path length (“small world phenomena”)3. Clustering (“community structure”)
11
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Measuring Real Networks: Degree distributions Problem: find the probability distribution that best fits
the observed data
degree
frequency
k
fk
fk = fraction of nodes with degree k = probability of a randomly selected node to have degree k
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Exponential distribution
Probability of having k neighbors
Identified by a line in the log-linear plot
p(k) = λe-λk
log p(k) = - λk + log λ
degree
log frequency
λ
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Power-law distributions
Right-skewed/Heavy-tail distribution there is a non-negligible fraction of nodes that has very high
degree (hubs) scale-free: f(ax) = bf(x), no characteristic scale, average is not
informative
p(k) = Ck-α
Power-law distribution gives a line in the log-log plot
α : power-law exponent (typically 2 ≤ α ≤ 3)
log p(k) = -α logk + logC
degree
frequency
log degree
log frequency
α
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
semilog
10
100
101
102
103
-4
10-3
10-2
10-1
100
loglog
This difference is particularly obvious if we plot them on a log vertical scale: for large x there are orders of magnitude differences between the two functions.
1cx)x(f
xc)x(f
50.cx)x(f
xc)x(f
50.cx)x(f
1cx)x(f
Network Science: Scale-Free Property February 7, 2011
Power Law vs. Exponential Distribution
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Internet Topology Primer
16
Internet backbone and regional connectivity
Multi-tier AS topology
Gateway Routers inside ASs
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Internet Degree Distribution
17
Holds for both AS and Router topologies
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Degree Distribution for Other Networks
18
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Power Law Exponent in Real Networks (M.
Newman 2003)
19
α : power-law exponent (typically 2 ≤ α ≤ 3)
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Measuring path length
dij = shortest path between i and jDiameter:
Average path length:
Also of interest: distribution of all shortest paths
ijji,
dmaxd
ji
ijd1)/2-n(n
1
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Path Length: Lattice Network
A total of n nodes arranged in a grid
Only neighbors (up,down,left,right) connected
Q: What is the diameter of the network?
A: 2 -1Q: What is the avg. distance?
i.e. picking two nodes randomly
A: It is in the order of (i.e. c )
21
n
n
n n
n
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Path Length: Random Geometric Network n wireless nodes in an area of
1x1 Each transmits at distance R
R must be at least for connectivity
Q: Choose two random nodes: What is the expected hop count (distance) between them?
A:
22
n
lognΟ
logn
nΟ
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Millgram’s small world experiment Letters were handed out to people in Nebraska to be
sent to a target in Boston People were instructed to pass on the letters to
someone they knew on first-name basis ~60 letters, only about 35% delivered
The letters that reached the destination followed paths of length around 6
Six degrees of separation: (play of John Guare)
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Millgram’s small world experiment: Email Version
In 2001, Duncan Watts, a professor at Columbia University, recreated Milgram's experiment using an e-mail message as the “package" that needed to be delivered.
Surprisingly, after reviewing the data collected by 48,000 senders and 19 targets in 157 different countries, Watts found that again the average number of intermediaries was 6.
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
A Few Good Men
Robert Wagner
Austin Powers: The spy who shagged me
Wild Things
Let’s make it legal
Barry Norton
What Price Glory
Monsieur Verdoux
Kevin Bacon number: link 2 actors in same movie
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Kevin Bacon Number
(statistics from IMDB) ~740000 linkable actors Average (path length) = 3 99% of actors less than 6 hops Try your own actor here:
http://www.cs.virginia.edu/oracle/26
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Erdos number: collaboration networks
Legendary mathematician Paul Erdos, around 1500 papers and 509 collaborators
Collaboration Graph: link between two authors who wrote a paper together
Erdos number of X: hop count between Erdos and author X in collaboration graph
~260,000 in connected component
27
Kostas Psounis
Kostas PsounisT. Spyropoulos
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Internet Path Lengths
28
Number of AS traversed by an email message• ~35000 nodes• Avg. path ~ 5!
Number of routers traversed by an email message• >200000• Avg. path ~ 15
plots taken from R. V. Hofstad
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Internet Path Length: Different Continents
29
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Measurement Findings: Path Length
Milgram’s experiment => Small World Phenomenon Short paths exist between most nodes: Path length l
<< total nodes N (e.g line network: path length l = O(N))
30
“Small world” = avg. path length l is at most logN
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Clustering (Transitivity) coefficient Measures the density of triangles (local clusters) in
the graph Two different ways to measure it:
The ratio of the means
i
i(1)
i nodeat centered triples
i nodeat centered trianglesC
1
23
4
5
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Example
1
23
4
5 83
6113
C(1)
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Clustering (Transitivity) coefficient Clustering coefficient for node i
The mean of the ratios
i nodeat centered triplesi nodeat centered triangles
Ci
i(2) C
n1
C
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Example
The two clustering coefficients give different measures
C(2) increases with nodes with low degree
1
23
4
5
3013
611151
C(2)
83
C(1)
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Clustering Coeff. In Real Nets (M. Newman 2003)
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Basic Graph Properties: Revision Material
36
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Summary of Findings
Most real networks have…
1. Short paths between nodes (“small world”)
2. Transitivity/Clustering coefficient that is finite > 0
3. Degree distribution that follows a power law
37
Q1. Can we design graph models that exhibit similar characteristics?Q2. Can we explain how/why these phenomena occur in the first place?Q3. Can we take advantage of these properties (e.g. searching, advertising, viral infection/immunization, etc.)?
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Undirected Graphs
Graph G=(V,E) V = set of vertices E = set of edges
1
2
3
45undirected graphE={(1,2),(1,3),(2,3),(3,4),(4,5)}
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Directed Graphs
Graph G=(V,E) V = set of vertices E = set of edges
1
2
3
45directed graphE={‹1,2›, ‹2,1› ‹1,3›, ‹3,2›, ‹3,4›, ‹4,5›}
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Weighted and Unweighted GraphsEdges have / do not have a weight associated
with them
weighted unweighted
48 13
5
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Undirected graph: Degree Distribution
1
2
3
45
degree d(i) of node inumber of edges
incident on node i
degree distribution1 node with degree 13 nodes with degree 21 node with degree 3P(1) = 1/5, P(2) = 3/5, P(3) =
1/5
23
1
degree1 2 3
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Undirected Graph: Degree Distribution
k
P(k)
1 2 3 4
0.10.20.30.40.50.6
Network Science: Graph Theory January 24, 2011
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Directed Graph: In- and Out-Degree
1
2
3
45
in-degree din(i) of node i number of edges pointing to
node i
out-degree dout(i) of node i number of edges leaving node i
in-degree sequence [1,2,1,1,1]
out-degree sequence [2,1,2,1,0]
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Paths Path from node i to node j: a sequence of edges
(directed or undirected from node i to node j) path length: number of edges on the path nodes i and j are connected cycle: a path that starts and ends at the same node
1
2
3
45
1
2
3
45
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Shortest Paths
Shortest Path from node i to node j also known as BFS path, or geodesic path
1
2
3
45
1
2
3
45
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Diameter
The longest shortest path in the graph
1
2
3
45
1
2
3
45
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Undirected graph: Components
1
2
3
45
Connected graph: a graph where every pair of nodes is connected
Disconnected graph: a graph that is not connected
Connected Components: subsets of vertices that are connected
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Fully Connected Graph
Clique Kn
A graph that has all possible n(n-1)/2 edges
1
2
3
45
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Directed Graph
1
2
3
45
Strongly connected graph: there exists a path from every i to every j
Weakly connected graph: If edges are made to be undirected the graph is connected
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Adjacency Matrix: Undirected Graph
Adjacency Matrix symmetric matrix for undirected graphs
1
2
3
45
01000
10100
01011
00101
00110
A
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Adjacency Matrix: Directed Graph
Adjacency Matrix non-symmetric matrix for undirected graphs
00000
10000
01010
00001
00110
A 1
2
3
45
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
0
1
1
1
1
0
1
1
1
1
0
1
1
1
1
0
0
1
0
1
0
0
0
1
0
0
1
1
0
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
1
0
G1
G2
G3
0
1 2
3
0
1
2
1
0
2
3
4
5
6
7
symmetric
undirected: n2/2directed: n2
Examples of Adjacency Matrices
Eurecom, Sophia-AntipolisThrasyvoulos Spyropoulos / [email protected]
Random Graph Models: Create/Explain Complex Network Properties
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Random Graph Models: Why do we Need Them? The networks discussed are quite large!
Impossible to describe or visualize explicitly.
Consider this example: You have a new Internet routing algorithm You want to evaluate it, but do not have a trace of the Internet
topology You decide to create an “Internet-like” graph on which you will
run your algorithm How do you describe/create this graph??
Random graphs: local and probabilistic rules by which vertices are connected
Goal: from simple probabilistic rules to observed complexity
Q: Which rules gives us (most of) the observed properties? 54
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Emergent Complexity in Cellular Automata
This is “Conway’s game of life” (many other automata) http://www.youtube.com/watch?v=ma7dwLIEiYU&feature=
related (demo)
http://www.bitstorm.org/gameoflife/ (try your own)56
Local Rules Each cell either white or blue Each cell interacts with its 8 neighbors Time is discrete (rounds)1. Any blue cell with fewer than two live
neighbors becomes white2. Any blue cell with two or three blue
neighbors lives on to the round3. Any blue cell with more than three blue
neighbors becomes white4. Any white cell with exactly three blue
neighbors become blue
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Back to Networks: (Erdös-Rényi) Random Graphs A very (very!) simple local rule:
(any) two vertices are connected with probability p Only inputs: number of vertices n and probability p
Denote this class of graphs as G(n,p)
57
Erdös-Rényi model (1960)
Connect with probability p
p=1/6 N=10
k ~ 1.5
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
N and p do not uniquely define the network– we can have many different realizations of it. How many?
P(G(N,L)) pL (1 p)N (N 1)
2 L
G(10,1/6)N=10 p=1/6
G(N,L): a graph with N nodes and L linksThe probability to form a particular graph G(N,L) is That is, each graph G(N,L)
appears with probability P(G(N,L)).
How Many Networks in G(n,p)?
2𝑁 (𝑁− 1)
2
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
P(L): the probability to have exactly L links in a network of N nodes and probability p:
P(L)N
2
L
pL (1 p)
N(N 1)
2 L
The maximum number of links in a network of N nodes.
Number of different ways we can choose L links among all potential
links.
Binomial distribution...
Relation of G(N,p) to G(N,L)
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
P(L): the probability to have a network of exactly L links
P(L)N
2
L
pL (1 p)
N(N 1)
2 L
L LP(L)pN(N 1)
2L0
N(N 1)
2
The average number of links <L> in a random graph
The standard deviation
2 p(1 p)N(N 1)
2)1( Npk
G(N,p) statistics
Average node degree <k>
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
As the network size increases, the distribution becomes increasingly narrow—which means that we are increasingly confident that the number of links the graph has is in the vicinity of <L>.
NO
NNp
p
L
1
)1(
212/1
G(N,p) as N ∞
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
The degree distribution follows a binomial average degree is <k> = p(N-1) variance σ2 = p(1-p)(N-1)
Assuming z=Np is fixed, as N → ∞,B(N,k,p) is approximated by a Poisson distribution
As N → ∞ Highly concentrated around the mean Probability of very high node degrees is exponentially small Very different from power law!
Random Graphs: Degree Distribution
zk
ek!
zz)P(k;p(k)
62
k1)(Nk p)(1pk
1Np)N,B(k;p(k)
1/2
1/2
k
1)(N
1
1)(N
1
p
p1
k
σ
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
The secret behind the small world effect – Looking at the network volume
ddS 4)(
Are Erdos-Renyi (Poisson) Graphs Small-World?
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
The secret behind the small world effect – Looking at the network volume
d
x
dddxdN1
2~)1(24)(
Polynomial growth
The Volume of Geometric Graphs
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
The secret behind the small world effect – Looking at the network volume
d
x
dddxdN1
2~)1(24)(
Polynomial growth
The Exploding Volume of Random Graphs
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
The secret behind the small world effect – Looking at the network volume
d
x
dddxdN1
2~)1(24)(
Polynomial growth
dd
x
dx k
k
kkdN ~
1
1)(
1
1
Exponential growth
The Exploding Volume of Random Graphs (2)
k
Nd
Nd
Nk
k
d
ln
ln
log
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
klog
Nloglmax
Given the huge differences in scope, size, and average degree, the agreement is excellent!
Distance in Random Graphs Compare with Real Data
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Random Graphs: Clustering Co-efficient
Consider a random graph G(n,p)Q: What is the probability that two of your neighbors are
also neighbors?A: It is equal to p, independent of local structure
clustering coefficient C = p
when z is fixed (sparse networks): C = z/n =O(1/n)
68
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Given the huge differences in scope, size, and average degree, there is a clear disagreement.
Clustering in Random Graphs Compare with Real Data
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Summary: Are Real Networks Random Graphs? Erdos-Renyi Graphs are “small world”
path lengths are O(logn)
Erdos-Renyi Graphs are not “scale-free” Degree distribution binomial and highly-concentrated (no
power-law) Exponentially small probability to have “hubs” (no heavy-tail)
Erdos-Renyi Graphs are not “clustered” C 0, as N becomes larger
Conclusion: ER random graphs are not a good model of real networks BUT: still provide a great deal of insight!
70
√
X
X
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Some of your neighbors neighbors are also your own
Exponential growth: k
Nd
ln
ln
dkdS
kS
kS
)(
)2(
)1(2
Clustering inhibits the small-worldness
pkkN
dSdSdkSdS dd 21
)2()1(1)1()(
)1()1(
)2()3(
11
)2(
)1(
1)0(
32
22
kpkN
kpkNkSS
pkN
kNkS
kS
S
Poisson Graph Diameter: Growth is slightly slower
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Small World Graphs: Watts-Strogatz Model Short paths must be combined with
High clustering coefficient
Watts and Strogatz model [WS98] Start with a ring, where every node is connected to the next k nodes With probability p, rewire every edge (or, add a shortcut) to a random
node
72
order randomness
p = 0 p = 10 < p < 1
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Small World Graphs (2)
The Watts Strogatz Model: It takes a lot of randomness to ruin the clustering, but a very small amount to overcome locality 73
log-scale in p
When p = 0, C = 3(k-2)/4(k-1) ~ ¾ L = n/k
For small p, C ~ ¾ L ~ logn
Clustering Coefficient – Characteristic Path Length
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Nodes: online user Links: email contact, tweet, or friendship
Alan Mislove, Measurement and Analysis of Online Social Networks
All distributions show a fat-tail behavior:there are orders of magnitude spread in the degrees
Online Social Networks
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
World Wide Web
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Scale-free Graphs: What About Power Laws? The configuration model
input: the degree sequence [d1,d2,…,dn] process:
- Create di copies of node i; link them randomly
- Take a random matching (pairing) of the copies• self-loops and multiple edges are allowed
76
4 1 3 2
But: Too artificial!
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Networks continuously expand by the addition of new nodes
Barabási & Albert, Science 286, 509 (1999)
ER, WS models: the number of nodes, N, is fixed (static models)
One Explanation of Scale-Free(ness): Growth
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
(1) Networks continuously expand by the addition of new nodes
Add a new node with m links
Barabási & Albert, Science 286, 509 (1999)
Growth Models
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Barabási & Albert, Science 286, 509 (1999)jj
ii k
kk
)(
PREFERENTIAL ATTACHMENT:
the probability that a node connects to a node with k links is proportional to k.
A: New nodes prefer to link to highly connected nodes.
Q: Where will the new node link to?ER, WS models: choose randomly.
Growth Models: Preferential Attachment
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Preferential Attachment in Networks
“The rich get richer”
First considered by [Price 65] as a model for citation networks each new paper is generated with m citations (on average) new papers cite previous papers with probability proportional
to their indegree (citations) what about papers without any citations?
- each paper is considered to have a “default” citation- probability of citing a paper with degree k, proportional to k+1
Power law with exponent α = 2+1/m
80
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Barabasi-Albert model
The BA model (undirected graph) input: some initial subgraph G0, and m the number of edges
per new node the process:
- nodes arrive one at the time- each node connects to m other nodes selecting them with probability
proportional to their degree- if [d1,…,dt] is the degree sequence at time t, the node t+1 links to
node i with probability
Results in power-law with exponent α = 3
Various Problems: cannot account for every power law observed (Web), correlates age with degree, etc.
81
2mtd
dd i
i i
i
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Poisson graphs as a function of p
As p increases, so does the density of the graph For small p (<0.2) notice that not all nodes are
connected For p = 0.2 only one isolated node
82
p = 0 p = 0.1 p = 0.2
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Phase Transitions in Random Graphs
We saw that increasing p denser networks In the large N case we increase z = Np the average degree
But what really happens as p (or z) increases?
83
A random network on 50 nodes:p = 0.01 disconnected, largest component = 3
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Phase Transitions in Random Graphs (2)
p = 0.03 large component appears But almost 40% of nodes still disconnected
84
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Phase Transitions in Random Graphs (3)
p = 0.05 “giant” component emerges Only 3 nodes disconnected Giant component the graph “percolates”
85
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
Phase Transitions in Random Graphs (4)
p = 0.10 all nodes connected
86
Thrasyvoulos Spyropoulos / [email protected] Eurecom, Sophia-Antipolis
S: the fraction of nodes in the giant component, S=NGC/N
there is a phase transition at <k>=1:
for <k> < 1 there is no giant component
for <k> > 1 there is a giant component
for large <k> the giant component contains all nodes (S=1)
http://linbaba.files.wordpress.com/2010/10/erdos-renyi.png
Connectivity (“Percolation”) of Random GraphsS
<k>