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Collaborators: Pip Pattison, Tom Snijders, Mark Handcock, Stanley Wasserman (among others)

A two minute introduction to: Exponential random graph (p *)models for social networks SNAC Workshop, Illinois, November 2005 Garry Robins, University of Melbourne, Australia. Collaborators: Pip Pattison, Tom Snijders, Mark Handcock, Stanley Wasserman (among others).

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Collaborators: Pip Pattison, Tom Snijders, Mark Handcock, Stanley Wasserman (among others)

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  1. A two minute introduction to:Exponential random graph (p*)models for social networksSNAC Workshop, Illinois, November 2005Garry Robins, University of Melbourne, Australia Collaborators: Pip Pattison, Tom Snijders, Mark Handcock, Stanley Wasserman (among others)

  2. Statistical modeling of endogenous network processes Guiding principles: Network ties are the outcome of (unobserved) social processes that tend to be local and interactive There are both regularities and irregularities in these local interactive processes We hence construct statistical models in which: local interactivity is permitted and assumptions about form of “local interactions” are explicit regularities are represented by model parameters and estimated from data consequences of local regularities for global network properties can be understood and can also provide an exacting approach to model evaluation

  3. Network topologies: what are the forms of local interactivity? Two tie variables are neighbours if: they share an actor Markov model (Frank & Strauss, 1986) they share connections realisation-dependent model with two existing ties (Pattison & Robins, 2002; (completing a social circuit) Snijders, Pattison, Robins & Handcock, 2005) There are other possibilities, but these two get us a long way

  4. Exponential random graph models P(X= x) = (1/c) exp{Q QzQ(x)} normalizing quantity parameter network statistic the summation is over all neighbourhoods Q Estimation of parameters: Markov Chain Monte Carlo Maximum Likelihood Models with nodal attributes are also possible: social selection; social influence

  5. Assumptions: two ties are neighbours: if they share an actor Markov if they complete a 4-cycle realisation-dependent* Configurations for neighbourhoods edge 2-star 3-star 4-star …triangle + ... 4-cycle 2-triangle and others Neighbourhoods depend on proximity assumptions

  6. New specifications(Snijders, Pattison, Robins & Handcock, 2005) k nodes 2-independent 3-independent … k-independent … 2-path 2-path 2-path k nodes triangle 2-triangle 3-triangle … k-triangle …

  7. Some current issues Work in progress: • Further work on model specification: directed networks; multiple networks; bipartite graphs • Incorporation of actor attributes • Efficiency of estimation Longer term goals: • Extend modeling to large-scale social systems, including cross-level interactions • Model estimation from sample data • Extend capacity to model network evolution, including new specifications • Co-evolution of psychological states and network structures

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