Infinite Hierarchical MMSB Model for Nested Communities/Groups in Social Networks
msra(2010)
摘要
Actors in realistic social networks play not one but a number of diverse
roles depending on whom they interact with, and a large number of such
role-specific interactions collectively determine social communities and their
organizations. Methods for analyzing social networks should capture these
multi-faceted role-specific interactions, and, more interestingly, discover the
latent organization or hierarchy of social communities. We propose a
hierarchical Mixed Membership Stochastic Blockmodel to model the generation of
hierarchies in social communities, selective membership of actors to subsets of
these communities, and the resultant networks due to within- and
cross-community interactions. Furthermore, to automatically discover these
latent structures from social networks, we develop a Gibbs sampling algorithm
for our model. We conduct extensive validation of our model using synthetic
networks, and demonstrate the utility of our model in real-world datasets such
as predator-prey networks and citation networks.
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关键词
gibbs sampling,social network,social communication
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