RT Journal Article
T1 Community detection in networks using bio-inspired optimization: 												Latest developments, new results and perspectives with a selection of recent meta-heuristics
A1 Osaba, Eneko
A1 Del Ser, Javier
A1 Camacho, David
A1 Bilbao, Miren Nekane
A1 Yang, Xin She
AB Detecting groups within a set of interconnected nodes is a widely addressed problem that can model a diversity of applications. Unfortunately, detecting the optimal partition of a network is a computationally demanding task, usually conducted by means of optimization methods. Among them, randomized search heuristics have been proven to be efficient approaches. This manuscript is devoted to providing an overview of community detection problems from the perspective of bio-inspired computation. To this end, we first review the recent history of this research area, placing emphasis on milestone studies contributed in the last five years. Next, we present an extensive experimental study to assess the performance of a selection of modern heuristics over weighted directed network instances. Specifically, we combine seven global search heuristics based on two different similarity metrics and eight heterogeneous search operators designed ad-hoc. We compare our methods with six different community detection techniques over a benchmark of 17 Lancichinetti–Fortunato–Radicchi network instances. Ranking statistics of the tested algorithms reveal that the proposed methods perform competitively, but the high variability of the rankings leads to the main conclusion: no clear winner can be declared. This finding aligns with community detection tools available in the literature that hinge on a sequential application of different algorithms in search for the best performing counterpart. We end our research by sharing our envisioned status of this area, for which we identify challenges and opportunities which should stimulate research efforts in years to come.
SN 1568-4946
YR 2020
FD 2020-02
LK https://hdl.handle.net/11556/4460
UL https://hdl.handle.net/11556/4460
LA eng
NO Osaba , E , Del Ser , J , Camacho , D , Bilbao , M N & Yang , X S 2020 , ' Community detection in networks using bio-inspired optimization : Latest developments, new results and perspectives with a selection of recent meta-heuristics ' , Applied Soft Computing Journal , vol. 87 , 106010 . https://doi.org/10.1016/j.asoc.2019.106010
NO Publisher Copyright: © 2019 Elsevier B.V.
NO Eneko Osaba and Javier Del Ser would like to thank the Basque Government, Spain for its funding support through the EMAITEK program. David Camacho also thanks the Spanish Ministry of Science , Education and Competitiveness (MINECO), Spain , the European Regional Development Fund (FEDER) and the Comunidad Autonoma de Madrid, Spain for their support under grants TIN2017-85727-C4-3-P (DeepBio) and P2018/TCS-4566 (CYNAMON). Eneko Osaba and Javier Del Ser would like to thank the Basque Government, Spain for its funding support through the EMAITEK program. David Camacho also thanks the Spanish Ministry of Science, Education and Competitiveness (MINECO), Spain, the European Regional Development Fund (FEDER) and the Comunidad Autonoma de Madrid, Spain for their support under grants TIN2017-85727-C4-3-P (DeepBio) and P2018/TCS-4566 (CYNAMON).
DS TECNALIA Publications
RD 28 jul 2024