Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Provable Estimation of the Number of Blocks in Block Models   Community detection is a fundamental unsupervised learning problem for
unlabeled networks which has a broad range of applications. Many community
detection algorithms assume that the number of clusters $r$ is known apriori.
In this paper, we propose an approach based on semi-definite relaxations, which
does not require prior knowledge of model parameters like many existing convex
relaxation methods and recovers the number of clusters and the clustering
matrix exactly under a broad parameter regime, with probability tending to one.
On a variety of simulated and real data experiments, we show that the proposed
method often outperforms state-of-the-art techniques for estimating the number
of clusters.

7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat