Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Node-centric community detection in multilayer networks with
  layer-coverage diversification bias   The problem of node-centric, or local, community detection in information
networks refers to the identification of a community for a given input node,
having limited information about the network topology. Existing methods for
solving this problem, however, are not conceived to work on complex networks.
In this paper, we propose a novel framework for local community detection based
on the multilayer network model. Our approach relies on the maximization of the
ratio between the community internal connection density and the external
connection density, according to multilayer similarity-based community
relations. We also define a biasing scheme that allows the discovery of local
communities characterized by different degrees of layer-coverage
diversification. Experimental evaluation conducted on real-world multilayer
networks has shown the significance of our approach.

25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph