If you have data measured in continuous time and you are interested in actor-based or tie-based models such as the ones estimated with SIENA or p*, you could use a regression approach (perhaps on a random sample from your network), e.g., see:
- Kossinets, G., & Watts, D. J. (2009). Origins of homophily in an evolving social network. American Journal of Sociology, 115(2), 405-450.
- de Nooy, W. (2011). Networks of action and events over time. A multilevel discrete-time event history model for longitudinal network data. Social Networks, In Press, Corrected Proof doi:DOI: 10.1016/j.socnet.2010.09.003 
Kind regards,


From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of ??
Sent: zondag 12 december 2010 14:29
To: [log in to unmask]
Subject: Questions about modeling the evolution of social network with over 30,000 nodes

***** To join INSNA, visit ***** Dear all:� 
I currently have a dataset of an online social network with over 30,000 nodes. I want to use the p* model or Snijders' stochastic actor-based model to estimate the evolution of the network. However, the current�programs�(e.g., SIENA) do not allow the input data larger than 1,000 nodes.�

Can anyone give me some advices on the model estimation?�


Dept. Media & Communication,
City University of Hong Kong

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