Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An iterative clustering algorithm for the Contextual Stochastic Block
  Model with optimality guarantees   Real-world networks often come with side information that can help to improve
the performance of network analysis tasks such as clustering. Despite a large
number of empirical and theoretical studies conducted on network clustering
methods during the past decade, the added value of side information and the
methods used to incorporate it optimally in clustering algorithms are
relatively less understood. We propose a new iterative algorithm to cluster
networks with side information for nodes (in the form of covariates) and show
that our algorithm is optimal under the Contextual Symmetric Stochastic Block
Model. Our algorithm can be applied to general Contextual Stochastic Block
Models and avoids hyperparameter tuning in contrast to previously proposed
methods. We confirm our theoretical results on synthetic data experiments where
our algorithm significantly outperforms other methods, and show that it can
also be applied to signed graphs. Finally we demonstrate the practical interest
of our method on real data.

6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph