Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model-Based Method for Social Network Clustering   We propose a simple mixed membership model for social network clustering in
this note. 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 by 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 for different types of simulated networks for assessing
the effectiveness of our algorithm. In the end, some concluding remarks and
future work are addressed briefly.

36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin