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CS8803-NS Network Science Fall 2013 Instructor: Constantine Dovrolis constantine@gatech.edu http://www.cc.gatech.edu/~dovrolis/Courses/NetSci/

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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. Disclaimers

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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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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?

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

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Emergence of giant connected component in G(n,p) as p increases http://networkx.lanl.gov/archive/networkx-1.1/examples/drawing/giant_component.html

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

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Emergence of giant connected component in G(n,p) as p increases https://www.youtube.com/watch?v=mpe 44sTSoF8 https://www.youtube.com/watch?v=mpe 44sTSoF8

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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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The configuration model

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http://mathinsight.org/generating_networks_desired_degree_distribution

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For instance, power-law degree with exponential cutoff

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Average path length

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Clustering coefficient in random networks with given degree distribution

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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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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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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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Preferential attachment http://www3.nd.edu/~networks/Linked/newfile11.htm

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Preferential attachment

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

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Avg path length in PA model

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Clustering in PA model

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

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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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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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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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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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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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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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