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CS8803-NS Network Science Fall 2013

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Presentation on theme: "CS8803-NS Network Science Fall 2013"— Presentation transcript:

1 CS8803-NS Network Science Fall 2013
Instructor: Constantine Dovrolis

2 Disclaimers The following slides include only the figures or videos that we use in class; they do not include detailed explanations, derivations or descriptions covered in class. Many of the following figures are copied from open sources at the Web. I do not claim any intellectual property for the following material.

3 Outline Network models – Why and how? Random network models
ER or Poisson random graphs (covered last week) Random graphs with given degree distribution Watts-Strogatz model for small-world networks Network models based on stochastic evolution Preferential attachment Variants of preferential attachment Preferential attachment for weighted networks Duplication-based models Network models based on optimization Fabrikant-Koutsoupias-Papadimitriou model Application paper: modeling the evolution of the proteome using a duplication-based model Discussion about network modeling

4 Network models – Why and how?
What does it mean to create a “network model”? What is the objective of this exercise? How do we know that a model is “realistic”? How do we know that a model is “useful”? How do we compare two models that seem equally realistic? Do we need models in our “brave new world” of big data?

5 Outline Network models – Why and how? Random network models
ER or Poisson random graphs (covered last week) Random graphs with given degree distribution Watts-Strogatz model for small-world networks Network models based on stochastic evolution Preferential attachment Variants of preferential attachment Preferential attachment for weighted networks Duplication-based models Network models based on optimization Fabrikant-Koutsoupias-Papadimitriou model Application paper: modeling the evolution of the proteome using a duplication-based model Discussion about network modeling

6 Reference point-1: ER random graphs
G(n,m) and G(n,p) models (see lecture notes for derivations)

7 Emergence of giant connected component in G(n,p) as p increases

8 Emergence of giant component
See lecture notes for derivation of the following

9 Emergence of giant connected component in G(n,p) as p increases
https://www.youtube.com/watch?v=mpe44sTSoF8

10 Outline Network models – Why and how? Random network models
ER or Poisson random graphs (covered last week) Random graphs with given degree distribution Watts-Strogatz model for small-world networks Network models based on stochastic evolution Preferential attachment Variants of preferential attachment Preferential attachment for weighted networks Duplication-based models Network models based on optimization Fabrikant-Koutsoupias-Papadimitriou model Application paper: modeling the evolution of the proteome using a duplication-based model Discussion about network modeling

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12 The configuration model

13 The configuration model

14 For instance, power-law degree with exponential cutoff

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16 Average path length

17 Clustering coefficient in random networks with given degree distribution

18 Outline Network models – Why and how? Random network models
ER or Poisson random graphs (covered last week) Random graphs with given degree distribution Watts-Strogatz model for small-world networks Network models based on stochastic evolution Preferential attachment Variants of preferential attachment Preferential attachment for weighted networks Duplication-based models Network models based on optimization Fabrikant-Koutsoupias-Papadimitriou model Application paper: modeling the evolution of the proteome using a duplication-based model Discussion about network modeling

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21 Here is a more important question:
Deriving an expression for the APL in this model has been proven very hard Here is a more important question: What is the minimum value of p for which we expect to see a small-world (logarithmic) path length? p >> 1/N

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23 Outline Network models – Why and how? Random network models
ER or Poisson random graphs (covered last week) Random graphs with given degree distribution Watts-Strogatz model for small-world networks Network models based on stochastic evolution Preferential attachment Variants of preferential attachment Preferential attachment for weighted networks Duplication-based models Network models based on optimization Fabrikant-Koutsoupias-Papadimitriou model Application paper: modeling the evolution of the proteome using a duplication-based model Discussion about network modeling

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26 Preferential attachment

27 Preferential attachment

28 Continuous-time model of PA (see class notes for derivations)

29 Avg path length in PA model

30 Clustering in PA model

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32 “Statistical mechanics of complex networks” by R. Albert and A-L
“Statistical mechanics of complex networks” by R.Albert and A-L.Barabasi

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34 Outline Network models – Why and how? Random network models
ER or Poisson random graphs (covered last week) Random graphs with given degree distribution Watts-Strogatz model for small-world networks Network models based on stochastic evolution Preferential attachment Variants of preferential attachment Preferential attachment for weighted networks Duplication-based models Network models based on optimization Fabrikant-Koutsoupias-Papadimitriou model Application paper: modeling the evolution of the proteome using a duplication-based model Discussion about network modeling

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39 Outline Network models – Why and how? Random network models
ER or Poisson random graphs (covered last week) Random graphs with given degree distribution Watts-Strogatz model for small-world networks Network models based on stochastic evolution Preferential attachment Variants of preferential attachment Preferential attachment for weighted networks Duplication-based models Network models based on optimization Fabrikant-Koutsoupias-Papadimitriou model Application paper: modeling the evolution of the proteome using a duplication-based model Discussion about network modeling

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44 Outline Network models – Why and how? Random network models
ER or Poisson random graphs (covered last week) Random graphs with given degree distribution Watts-Strogatz model for small-world networks Network models based on stochastic evolution Preferential attachment Variants of preferential attachment Preferential attachment for weighted networks Duplication-based models Network models based on optimization Fabrikant-Koutsoupias-Papadimitriou model Application paper: modeling the evolution of the proteome using a duplication-based model Discussion about network modeling

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53 Outline Network models – Why and how? Random network models
ER or Poisson random graphs (covered last week) Random graphs with given degree distribution Watts-Strogatz model for small-world networks Network models based on stochastic evolution Preferential attachment Variants of preferential attachment Preferential attachment for weighted networks Duplication-based models Network models based on optimization Fabrikant-Koutsoupias-Papadimitriou model Application paper: modeling the evolution of the proteome using a duplication-based model Discussion about network modeling

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59 Outline Network models – Why and how? Random network models
ER or Poisson random graphs (covered last week) Random graphs with given degree distribution Watts-Strogatz model for small-world networks Network models based on stochastic evolution Preferential attachment Variants of preferential attachment Preferential attachment for weighted networks Duplication-based models Network models based on optimization Fabrikant-Koutsoupias-Papadimitriou model Application paper: modeling the evolution of the proteome using a duplication-based model Discussion about network modeling

60 Discussion about network models
Random? Stochastic evolution? Optimization-based? How to choose? When does it matter? How do we compare two models that seem equally realistic? “All models are wrong but some are useful” But when is a model useful?


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