Strategies for online inference of model-based clustering in large and growing networks

Details Strategies for online inference of model-based clustering in large and growing networks Strategies for online inference of model-based clustering in large and growing networks

2009-10-11

arxiv.org/abs/0910.2034v2

Annals of Applied Statistics 2010, Vol. 4, No. 2, 687-714

Statistics

Applications

Published in at http://dx.doi.org/10.1214/10-AOAS359 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/10-AOAS359

In this paper we adapt online estimation strategies to perform model-based clustering on large networks. Our work focuses on two algorithms, the first based on the SAEM algorithm, and the second on variational methods. These two strategies are compared with existing approaches on simulated and real data. We use the method to decipher the connexion structure of the political websphere during the US political campaign in 2008. We show that our online EM-based algorithms offer a good trade-off between precision and speed, when estimating parameters for mixture distributions in the context of random graphs.

Affiliated with

Also associated with

No associations

Rating

Strategies for online inference of model-based clustering in large and growing networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Strategies for online inference of model-based clustering in large and growing networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Strategies for online inference of model-based clustering in large and growing networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-99271