Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Overlapping Community Detection with Graph Neural Networks   Community detection is a fundamental problem in machine learning. While deep
learning has shown great promise in many graphrelated tasks, developing neural
models for community detection has received surprisingly little attention. The
few existing approaches focus on detecting disjoint communities, even though
communities in real graphs are well known to be overlapping. We address this
shortcoming and propose a graph neural network (GNN) based model for
overlapping community detection. Despite its simplicity, our model outperforms
the existing baselines by a large margin in the task of community recovery. We
establish through an extensive experimental evaluation that the proposed model
is effective, scalable and robust to hyperparameter settings. We also perform
an ablation study that confirms that GNN is the key ingredient to the power of
the proposed model.

10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph