lecture 2: network properties cs 790g: complex networks slides are modified from networks: theory...
TRANSCRIPT
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Lecture 2:
Network properties
CS 790g: Complex Networks
Slides are modified from Networks: Theory and Application by Lada Adamic
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Outline
What is a network? a bunch of nodes and edges
How do you characterize it? with some basic network metrics
Network models
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What are networks?
Networks are collections of points joined by lines.
“Network” ≡ “Graph”
points lines
vertices edges, arcs math
nodes links computer science
sites bonds physics
actors ties, relations sociology
node
edge
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Network elements: edges
Directed (also called arcs) A -> B
A likes B, A gave a gift to B, A is B’s child
Undirected A <-> B or A – B
A and B like each other A and B are siblings A and B are co-authors
Edge attributes weight (e.g. frequency of communication) ranking (best friend, second best friend…) type (friend, relative, co-worker) properties depending on the structure of the rest of the graph:
e.g. betweenness
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Directed networks
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Ada
Cora
Louise
Jean
Helen
Martha
Alice
Robin
Marion
Maxine
Lena
Hazel Hilda
Frances
Eva
RuthEdna
Adele
Jane
Anna
Mary
Betty
Ella
Ellen
Laura
Irene
girls’ school dormitory dining-table partners (Moreno, The sociometry reader, 1960)
first and second choices shown
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Edge weights can have positive or negative values
One gene activates/ inhibits another
One person trusting/ distrusting another
Research challenge: How does one
‘propagate’ negative feelings in a social network?
Is my enemy’s enemy my friend?
Transcription regulatory network in baker’s yeast
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Adjacency matrices
Representing edges (who is adjacent to whom) as a matrix Aij = 1 if node i has an edge to node j
= 0 if node i does not have an edge to j
Aii = 0 unless the network has self-loops
Aij = Aji if the network is undirected,or if i and j share a reciprocated edge
ij
i
ij
1
2
3
4
Example:
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0 0 0 0 0
0 0 1 1 0
0 1 0 1 0
0 0 0 0 1
1 1 0 0 0
A =
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Adjacency lists
Edge list 2 3 2 4 3 2 3 4 4 5 5 2 5 1
Adjacency list is easier to work with if network is
large sparse
quickly retrieve all neighbors for a node 1: 2: 3 4 3: 2 4 4: 5 5: 1 2
1
2
3
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Nodes
Node network properties from immediate connections
indegreehow many directed edges (arcs) are incident on a node
outdegreehow many directed edges (arcs) originate at a node
degree (in or out)number of edges incident on a node
outdegree=2
indegree=3
degree=5
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Node degree from matrix values
Outdegree =0 0 0 0 0
0 0 1 1 0
0 1 0 1 0
0 0 0 0 1
1 1 0 0 0
A =
n
jijA
1
example: outdegree for node 3 is 2, which we obtain by summing the number of non-zero entries in the 3rd row
Indegree =0 0 0 0 0
0 0 1 1 0
0 1 0 1 0
0 0 0 0 1
1 1 0 0 0
A =
n
iijA
1
example: the indegree for node 3 is 1, which we obtain by summing the number of non-zero entries in the 3rd column
n
iiA
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n
jjA
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Characterizing networks:Is everything connected?
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Network metrics: connected components
Strongly connected components Each node within the component can be reached from every other node
in the component by following directed links
Strongly connected components B C D E A G H F
Weakly connected components: every node can be reached from every other node by following links in either direction
A
B
C
DE
FG
H
A
B
C
DE
FG
H
Weakly connected components A B C D E G H F
In undirected networks one talks simply about ‘connected components’
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network metrics: size of giant component
if the largest component encompasses a significant fraction of the graph, it is called the giant component
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Outline
What is a network? a bunch of nodes and edges
How do you characterize it? with some basic network metrics
Network models
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Structural Metrics
Degree distribution
Average path length
Centrality Betweenness Closeness
Graph density Clustering coefficient
Several other graph metrics exist Assortativity Modularity …
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degree sequence and degree distribution
Degree sequence: An ordered list of the (in,out) degree of each node
In-degree sequence: [2, 2, 2, 1, 1, 1, 1, 0]
Out-degree sequence: [2, 2, 2, 2, 1, 1, 1, 0]
(undirected) degree sequence: [3, 3, 3, 2, 2, 1, 1, 1]
Degree distribution: A frequency count of the occurrence of each degree
In-degree distribution: [(2,3) (1,4) (0,1)]
Out-degree distribution: [(2,4) (1,3) (0,1)]
(undirected) distribution: [(3,3) (2,2) (1,3)]
0 1 20
1
2
3
4
5
indegree
fre
qu
en
cy
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Structural Metrics:Degree distribution
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Characterizing networks:How far apart are things?
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Structural metrics: Average path length
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Characterizing networks:Who is most central?
?
?
?
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Centrality: betweenness
The fraction of all directed paths between any two vertices that pass through a node
Normalization undirected: (N-1)*(N-2)/2 directed graph: (N-1)*(N-2) e.g.
CB (ni) g jkj ,k
(i) /g jk
betweenness of vertex i paths between j and k that pass through i
all paths between j and k
CB
' (ni) CB(i) /[(N 1)(N 2)]
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Centrality: closeness
How close the vertex is to others depends on inverse distance to other vertices
Cc (i) d(i , j)j1
g
1
CC' (i) (CC (i))(n 1)
Normalization
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network metrics: graph density
Of the connections that may exist between n nodes directed graph
emax = n*(n-1)
undirected graphemax = n*(n-1)/2
What fraction are present? density = e/ emax
For example, out of 12 possible connections,
this graph has 7, giving it a density of 7/12 = 0.583
Would this measure be useful for comparing networks of different sizes (different numbers of nodes)?
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Structural Metrics:Clustering coefficient
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Outline
What is a network? a bunch of nodes and edges
How do you characterize it? with some basic network metrics
Network models
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Four structural models
Regular networks
Random networks
Small-world networks
Scale-free networks
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Regular networks – fully connected
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Regular networks – Lattice
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Regular networks – Lattice: ring world
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modeling networks: random networks
Nodes connected at random Number of edges incident on each node is Poisson
distributed
Poisson distribution
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Random networks
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Erdos-Renyi random graphs
What happens to the size of the giant component as the density of the network increases?
http://ccl.northwestern.edu/netlogo/models/run.cgi?GiantComponent.884.53432
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Random Networks
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modeling networks: small worlds
Small worlds a friend of a friend is also
frequently a friend but only six hops separate any
two people in the world
Arnold S. – thomashawk, Flickr;
http://creativecommons.org/licenses/by-nc/2.0/deed.en34
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Small world models
Duncan Watts and Steven Strogatz a few random links in an otherwise structured graph make the
network a small world: the average shortest path is short
regular lattice:
my friend’s friend is
always my friend
small world:
mostly structured
with a few random
connections
random graph:
all connections
random
Source: Watts, D.J., Strogatz, S.H.(1998) Collective dynamics of 'small-world' networks. Nature 393:440-442. 35
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Watts Strogatz Small World Model
As you rewire more and more of the links and random, what happens to the clustering coefficient and average shortest path relative to their values for the regular lattice?
http://projects.si.umich.edu/netlearn/NetLogo4/SmallWorldWS.html36
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Small-world networks
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Scale-free networks
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Scale-free networks
Many real world networks contain hubs: highly connected nodes
Usually the distribution of edges is extremely skewed
many nodes with few edges
fat tail: a few nodes with a very large numberof edges
no “typical” number of edges
number of edges
num
ber
of n
odes
with
so
man
y ed
ges
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But is it really a power-law?
A power-law will appear as a straight line on a log-log plot:
A deviation from a straight line could indicate a different distribution: exponential lognormal
log(# edges)
log(
# n
odes
)
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Scale-free networks
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