M-Link: a link clustering memetic algorithm for overlapping community detection

Gabardo, Ademir Cristiano; Berretta, Regina; Moscato, Pablo

doi:10.1007/s12293-020-00300-x

Cited by 13 publications

(5 citation statements)

References 28 publications

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“…Detecting and tracking athletes in sports videos can obtain athletes' sports parameters, analyze athletes' behavior characteristics, and judge the standardization of athletes' actions, which is a necessary step for analyzing sports videos. Through the target detection and tracking of the game video, professionals can get the relevant data they need [ 8 , 9 ]. The existing research needs too many features, and the recognition speed is slow, which cannot meet the real-time requirements well.…”

Section: Introductionmentioning

confidence: 99%

Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm

Zhong

Dong

2022

Computational Intelligence and Neuroscience

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This paper constructs a sports action recognition model based on deep learning (DL) and clustering extraction algorithm. For the input detection image frame, athletes’ movements are detected through DL network, and then athletes’ sports movements are fused. Moreover, it expands new knowledge and improves learning ability through automatic learning training set. The neural network (NN) is applied to the sample set containing images of nonathletes, and the negative training sample set is iteratively enhanced according to the generated false positives, and the results are optimized by clustering method. Simulation experiments show that compared with other algorithms, the clustering extraction algorithm in this paper has achieved superior performance in recognition rate and false alarm rate, and the recognition speed is faster. The aim is to extract the athletes’ training postures through the analysis of sports movements, so as to assist coaches to train athletes more professionally and provide some reference for sports movement recognition.

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Section: Introductionmentioning

confidence: 99%

Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm

Zhong

Dong

2022

Computational Intelligence and Neuroscience

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“…In 2019, a regression approach based on 'Continued Fraction' (CFR) was proposed; it views multivariate regression as a non-linear optimisation problem and the authors used a memetic algorithm to find approximations to the unknown target functions from experimental data (Sun & Moscato, 2019). Memetic algorithms are a population-based approach to solve computational problems that J o u r n a l P r e -p r o o f are posed as optimisation tasks and have been heavily used for other data analytics in combinatorial optimisation problems (Gabardo et al, 2020;Zaher et al, 2019;Haque & Moscato, 2019) and that are also showing impressive results…”

Section: Continued Fraction Regressionmentioning

confidence: 99%

Multiple regression techniques for modelling dates of first performances of Shakespeare-era plays

Moscato¹,

Craig²,

Egan³

et al. 2022

Expert Systems with Applications

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“…Finally, a node belongs to a node community

L

if at least one of its incident links belongs to the link community

normalL

(Ahn et al, 2010; Evans & Lambiotte, 2009; Pereira‐Leal et al, 2004). In 2020, Gabardo et al (2020) proposed a link‐based clustering approach using the memetic algorithm, called M‐Link, to unveil overlapping communities. M‐Link aims to maximize the link partition density fitness function.…”

Section: Related Workmentioning

confidence: 99%

Overlapping community detection in complex networks using fuzzy theory, balanced link density, and label propagation

2021

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Complex networks represent various real-world systems. Overlapping community detection is one of the critical tasks in studying these networks and has significance to a wide variety of applications, including the exploration of online social networks because of the natural attitude of persons to participate in multiple communities at the same time. Despite a large number of existing community detection algorithms for detecting disjoint communities, the efficient and fast uncovering of overlapping communities has remained a challenging problem. To provide an efficient solution, on the one hand, the balanced link density label propagation (BLDLP) algorithm, proposed by the authors of the current study, is a fast, stable, and efficient method for disjoint community detection. On the other hand, the fuzzy theory is a worthwhile approach for overlapping community detection since it provides the membership rate of the overlapping nodes as well as the detection of overlapping communities. Hence, in this paper, based on the synergy of the BLDLP algorithm and the fuzzy theory, a novel method, called fuzzy BLDLP, for overlapping community detection is proposed.Fuzzy BLDLP is fast and efficient. The proposed method needs no prior information about the number of network communities to discover them. The experiments on both synthetic and real-world known networks, including Zachary, Dolphins, and COVID-19 Co-authorship, have revealed that the proposed method successfully detects the overlapping nodes and communities and hence is comparable with the state-of-the-art overlapping community detection algorithms in terms of recall, precision, F-score and overlapping normalized mutual information.

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M-Link: a link clustering memetic algorithm for overlapping community detection

Cited by 13 publications

References 28 publications

Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm

Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm

Multiple regression techniques for modelling dates of first performances of Shakespeare-era plays

Overlapping community detection in complex networks using fuzzy theory, balanced link density, and label propagation

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