Community Detection in Weighted Directed Networks Using Nature-Inspired Heuristics

Osaba, Eneko; Ser, Javier Del; Camacho, David; Gálvez, Antonio; Iglesias, Andrés; Fister, Iztok; Fister, Iztok

doi:10.1007/978-3-030-03496-2_36

Cited by 5 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main rationale behind the use of these nature-inspired metaheuristics relies in their reputed efficiency for solving other combinatorial optimization problems along recent years. Some examples of successful application cases can be found in [23,25,27,28]. The good acceptance of these previous studies have led us to the hypothesis that these methods may be promising also for the problem approached in this work.…”

Section: Introductionmentioning

confidence: 69%

Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks

Masegosa

Osaba

Angarita-Zapata

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

Self Cite

View full text Add to dashboard Cite

Connected vehicles are revolutionizing the way in which transport and mobility are conceived. The main technology behind are the socalled Vehicular Ad-Hoc Networks (VANETs), which are communication networks that connect vehicles and facilitate various services. Usually, these services require a centralized architecture where the main server collects and disseminates information from/to vehicles. In this paper, we focus on improving the downlink information dissemination in VANETs with this centralized architecture. With this aim, we model the problem as a Vertex Covering optimization problem and we propose four new nature-inspired methods to solve it: Bat Algorithm (), Firefly Algorithm, Particle Swarm Optimization, and Cuckoo Search. The new methods are tested over data from a real scenario. Results show that these metaheuristics, especially Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization, can be considered as powerful solvers for improving information dissemination in VANETs.

show abstract

Section: Introductionmentioning

confidence: 69%

Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks

Masegosa

Osaba

Angarita-Zapata

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Genetic Algorithms [2, [14][15][16][17], which are particularly repetitive meta-heuristics inspired by the theory of natural evolution; •…”

Section: Network Topology Orientedmentioning

confidence: 99%

“…Handle the network connectivity information, expressed in vectors, as part of each corresponding user node information [22,23,31]; • Construct content edges by selecting the top K neighbors of each vertex using the node attribute information contained [10,13]; • Optimize a unified objective function [24,32]; and • Leverage the well-known Swarm Intelligence methods such as BA (Bat Algorithm) [14,17], FA (Firefly Algorithm) [14,16] and PeSOA (Penguins Search Optimization) [15] to define bio-inspired metaheuristics schemes that use the evolution mechanisms to proceed to the detection of underlying communities.…”

Section: Hybrid Approachesmentioning

confidence: 99%

“…In particular, based on the homophily concept, which is the natural human tendency for interacting with peers of similar interests and characteristics [9], it is more than obvious that a valuable community should be the group of associated vertices that besides being more densely intra-connected, should also have various distinctive node attributes in common [3]. Thus, plentiful hybrid methods [3,7,[10][11][12][13][14][15][16][17] have been published to uncover communities, leveraging not only the vertices associations but also the inherent node attribute information.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, the more sophisticated hybrid methods [7,[13][14][15][16][17][18][22][23][24][25][26][27][28] try to combine all the intrinsic information modalities by defining either global probabilistic inference-based models or global heuristic measure-based models to unveil the underlying communities. Even if their generated outcome is considered more accurate and further qualitative comparing to the respective of classic algorithms, since all the different information aspects are taken into account, their application is limited to particularly small networks due to the exhaustive statistical analysis required.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Distributed Hybrid Community Detection Methodology for Social Networks

2019

View full text Add to dashboard Cite

Nowadays, the amount of digitally available information has tremendously grown, with real-world data graphs outreaching the millions or even billions of vertices. Hence, community detection, where groups of vertices are formed according to a well-defined similarity measure, has never been more essential affecting a vast range of scientific fields such as bio-informatics, sociology, discrete mathematics, nonlinear dynamics, digital marketing, and computer science. Even if an impressive amount of research has yet been published to tackle this NP-hard class problem, the existing methods and algorithms have virtually been proven inefficient and severely unscalable. In this regard, the purpose of this manuscript is to combine the network topology properties expressed by the loose similarity and the local edge betweenness, which is a currently proposed Girvan–Newman’s edge betweenness measure alternative, along with the intrinsic user content information, in order to introduce a novel and highly distributed hybrid community detection methodology. The proposed approach has been thoroughly tested on various real social graphs, roundly compared to other classic divisive community detection algorithms that serve as baselines and practically proven exceptionally scalable, highly efficient, and adequately accurate in terms of revealing the subjacent network hierarchy.

show abstract