Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Community detection in sparse networks via Grothendieck's inequality   We present a simple and flexible method to prove consistency of semidefinite
optimization problems on random graphs. The method is based on Grothendieck's
inequality. Unlike the previous uses of this inequality that lead to constant
relative accuracy, we achieve any given relative accuracy by leveraging
randomness. We illustrate the method with the problem of community detection in
sparse networks, those with bounded average degrees. We demonstrate that even
in this regime, various simple and natural semidefinite programs can be used to
recover the community structure up to an arbitrarily small fraction of
misclassified vertices. The method is general; it can be applied to a variety
of stochastic models of networks and semidefinite programs.

4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas