@Article{ChagasLoreSant:2022:HyHeOv,
author = "Chagas, Guilherme Oliveira and Lorena, Luiz Antonio Nogueira and
Santos, Rafael Duarte Coelho dos",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "A hybrid heuristic for overlapping community detection through the
conductance minimization",
journal = "Physica A: Statistical Mechanics and its Applications",
year = "2022",
volume = "592",
pages = "e126887",
month = "Apr.",
keywords = "Conductance minimization, Hybrid heuristic, Overlapping community
detection.",
abstract = "Community structures, which are sets of elements that share some
relationship between themselves, can be found in several
real-world networks. Many of these communities, also known as
clusters, can share elements, i.e., they may overlap. Identifying
such overlapping clusters is usually a harder task than finding
non-overlapping ones and, therefore, it needs more sophisticated
methods. In this work we proposed a hybrid heuristic for detecting
overlapping clusters in networks. An overlapping clustering is
generated through the solving of a mixed-integer linear program
using, as input, a heterogeneous set of good-quality clusters.
This set is produced by two state-of-the-art overlapping community
detection algorithms. In addition, some local search methods for
conductance minimization are used to improve the quality of the
clustering generate by our hybrid heuristic. Test results in
artificial and real-world graphs show that our approach is able to
detect overlapping clusters with better overall conductance than
methods in the state of the art.",
doi = "10.1016/j.physa.2022.126887",
url = "http://dx.doi.org/10.1016/j.physa.2022.126887",
issn = "0378-4371",
language = "en",
targetfile = "1-s2.0-S0378437122000231-2022.pdf",
urlaccessdate = "29 jun. 2024"
}