Models and Algorithms for Complex Networks Networks and Measurements Lecture 3.
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Transcript of Models and Algorithms for Complex Networks Networks and Measurements Lecture 3.
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Models and Algorithms for Complex Networks
Networks and MeasurementsLecture 3
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Types of networks
Social networks Knowledge (Information) networks Technology networks Biological networks
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Social Networks
Links denote a social interaction Networks of acquaintances collaboration networks
• actor networks• co-authorship networks• director networks
phone-call networks e-mail networks IM networks Bluetooth networks sexual networks home page/blog networks
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Knowledge (Information) Networks
Nodes store information, links associate information Citation network (directed acyclic) The Web (directed) Peer-to-Peer networks Word networks Networks of Trust Software graphs
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Technological networks
Networks built for distribution of commodity The Internet
• router level, AS level
Power Grids Airline networks Telephone networks Transportation Networks
• roads, railways, pedestrian traffic
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Biological networks
Biological systems represented as networks Protein-Protein Interaction Networks Gene regulation networks Gene co-expression networks Metabolic pathways The Food Web Neural Networks
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Measuring Networks
Degree distributions Small world phenomena Clustering Coefficient Mixing patterns Degree correlations Communities and clusters
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The basic random graph model
The measurements on real networks are usually compared against those on “random networks”
The basic Gn,p (Erdös-Renyi) random graph model: n : the number of vertices 0 ≤ p ≤ 1 for each pair (i,j), generate the edge (i,j)
independently with probability p
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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
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Power-law distributions
The degree distributions of most real-life networks follow a power law
Right-skewed/Heavy-tail distribution there is a non-negligible fraction of nodes that has very high
degree (hubs) scale-free: no characteristic scale, average is not informative
In stark contrast with the random graph model! Poisson degree distribution, z=np
highly concentrated around the mean the probability of very high degree nodes is exponentially
small
p(k) = Ck-α
zk
ek!z
z)P(k;p(k)
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Power-law signature
Power-law distribution gives a line in the log-log plot
α : power-law exponent (typically 2 ≤ α ≤ 3)
degree
frequency
log degree
log frequency α
log p(k) = -α logk + logC
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Examples
Taken from [Newman 2003]
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A random graph example
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Exponential distribution
Observed in some technological or collaboration networks
Identified by a line in the log-linear plot
p(k) = λe-λk
log p(k) = - λk + log λ
degree
log frequency λ
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Average/Expected degree
For random graphs z = np
For power-law distributed degree if α ≥ 2, it is a constant if α < 2, it diverges
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Maximum degree
For random graphs, the maximum degree is highly concentrated around the average degree z
For power law graphs
Rough argument: solve nP[X≥k]=1
1)1/(αmax nk
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Collective Statistics (M. Newman 2003)
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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
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Example
1
2
3
4
583
6113
C(1)
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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
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Example
The two clustering coefficients give different measures
C(2) increases with nodes with low degree
1
2
3
4
5
3013
611151
C(2)
83
C(1)
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Collective Statistics (M. Newman 2003)
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Clustering coefficient for random graphs
The probability of two of your neighbors also being neighbors is p, independent of local structure clustering coefficient C = p when z is fixed C = z/n =O(1/n)
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The C(k) distribution
The C(k) distribution is supposed to capture the hierarchical nature of the network when constant: no hierarchy when power-law: hierarchy
degreek
C(k)
C(k) = average clustering coefficientof nodes with degree k
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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
The letters that reached the destination followed paths of length around 6
Six degrees of separation: (play of John Guare)
Also: The Kevin Bacon game The Erdös number
Small world project: http://smallworld.columbia.edu/index.html
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Measuring the small world phenomenon
dij = shortest path between i and j Diameter:
Characteristic path length:
Harmonic mean
Also, distribution of all shortest paths
ijji,
dmaxd
ji
ijd1)/2-n(n
1
ji
1-ij
d1)/2-n(n
11
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Collective Statistics (M. Newman 2003)
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Is the path length enough?
Random graphs have diameter
d=logn/loglogn when z=ω(logn)
Short paths should be combined with other properties ease of navigation high clustering coefficient
logzlogn
d
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Degree correlations
Do high degree nodes tend to link to high degree nodes?
Pastor Satoras et al. plot the mean degree of the neighbors as a function of
the degree
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Degree correlations
Newman compute the correlation coefficient of
the degrees of the two endpoints of an edge
assortative/disassortative
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Collective Statistics (M. Newman 2003)
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Connected components
For undirected graphs, the size and distribution of the connected components is there a giant component?
For directed graphs, the size and distribution of strongly and weakly connected components
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Network Resilience
Study how the graph properties change when performing random or targeted node deletions
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Graph eigenvalues
For random graphs semi-circle law
For the Internet (Faloutsos3)
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Motifs
Most networks have the same characteristics with respect to global measurements can we say something about the local
structure of the networks?
Motifs: Find small subgraphs that over-represented in the network
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Example
Motifs of size 3 in a directed graph
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Finding interesting motifs
Sample a part of the graph of size S Count the frequency of the motifs of
interest Compare against the frequency of
the motif in a random graph with the same number of nodes and the same degree distribution
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Generating a random graph
Find edges (i,j) and (x,y) such that edges (i,y) and (x,j) do not exist, and swap them repeat for a large enough number of
timesi j
xy
G
i j
xy
G-swappeddegrees of i,j,x,yare preserved
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The feed-forward loop
Over-represented in gene-regulation networks a signal delay mechanism
X
Y Z
Milo et al. 2002
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Families of networks
Compute the relative frequency of different motifs, and group the networks if they exhibit similar frequencies
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Experiments
Milo et al. 2004
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References
M. E. J. Newman, The structure and function of complex networks, SIAM Reviews, 45(2): 167-256, 2003
R. Albert and A.-L. Barabási, Statistical mechanics of complex networks, Reviews of Modern Physics 74, 47-97 (2002).
S. N. Dorogovstev and J. F. F. Mendez, Evolution of Networks: From Biological Nets to the Internet and WWW.
Michalis Faloutsos, Petros Faloutsos and Christos Faloutsos. On Power-Law Relationships of the Internet Topology. ACM SIGCOMM 1999.
E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A.-L. Barabási, Hierarchical organization of modularity in metabolic networks, Science 297, 1551-1555 (2002).
R Milo, S Shen-Orr, S Itzkovitz, N Kashtan, D Chklovskii & U Alon, Network Motifs: Simple Building Blocks of Complex Networks. Science, 298:824-827 (2002).
R Milo, S Itzkovitz, N Kashtan, R Levitt, S Shen-Orr, I Ayzenshtat, M Sheffer & U Alon, Superfamilies of designed and evolved networks. Science, 303:1538-42 (2004).