Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mixed-Membership Stochastic Block-Models for Transactional Networks   Transactional network data can be thought of as a list of one-to-many
communications(e.g., email) between nodes in a social network. Most social
network models convert this type of data into binary relations between pairs of
nodes. We develop a latent mixed membership model capable of modeling richer
forms of transactional network data, including relations between more than two
nodes. The model can cluster nodes and predict transactions. The block-model
nature of the model implies that groups can be characterized in very general
ways. This flexible notion of group structure enables discovery of rich
structure in transactional networks. Estimation and inference are accomplished
via a variational EM algorithm. Simulations indicate that the learning
algorithm can recover the correct generative model. Interesting structure is
discovered in the Enron email dataset and another dataset extracted from the
Reddit website. Analysis of the Reddit data is facilitated by a novel
performance measure for comparing two soft clusterings. The new model is
superior at discovering mixed membership in groups and in predicting
transactions.

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The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an