Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Infinite Multiple Membership Relational Modeling for Complex Networks   Learning latent structure in complex networks has become an important problem
fueled by many types of networked data originating from practically all fields
of science. In this paper, we propose a new non-parametric Bayesian
multiple-membership latent feature model for networks. Contrary to existing
multiple-membership models that scale quadratically in the number of vertices
the proposed model scales linearly in the number of links admitting
multiple-membership analysis in large scale networks. We demonstrate a
connection between the single membership relational model and multiple
membership models and show on "real" size benchmark network data that
accounting for multiple memberships improves the learning of latent structure
as measured by link prediction while explicitly accounting for multiple
membership result in a more compact representation of the latent structure of
networks.

37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat