  We propose a simple mixed membership model for social network clustering in
this paper. A flexible function is adopted to measure affinities among a set of
entities in a social network. The model not only allows each entity in the
network to possess more than one membership, but also provides accurate
statistical inference about network structure. We estimate the membership
parameters using an MCMC algorithm. We evaluate the performance of the proposed
algorithm by applying our model to two empirical social network data, the
Zachary club data and the bottlenose dolphin network data. We also conduct some
numerical studies based on synthetic networks for further assessing the
effectiveness of our algorithm. In the end, some concluding remarks and future
work are addressed briefly.
