Community Detection Utilizing a Novel Multi-swarm Fruit Fly Optimization Algorithm with Hill-Climbing Strategy

Liu, Qiang; Zhou, Bin; Li, Shudong; Li, Aiping; Zou, Peng; Jia, Yan

doi:10.1007/s13369-015-1905-5

Cited by 20 publications

(5 citation statements)

References 37 publications

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“…The performance of CD-OPGPBA is compared with many state-of-the-art static community detection methods, including ECSD [26], FN [27], GN [28], Meme-net [29], walktrap [31], CNM [32], BGLL [33], MSFCM [34], FMM/H1 [35], Informap [36], LAP_CL [37], TNS-LPA [38], ECES [7], MAGA-net [30], GATHB [39], MOGA [40], ECGA [41], CC-GA [42], MOEA/D [43], VOLUME 10, 2022 DBA [44], ACODCS [45], MPSOA [46], MODPSO [47], DECD [48], CCDECD [49], IDDE [50], CDMFOA [51], Com-MOEA/D [57], CDEMO [52], MDSTA [53], and SOSCD [54]. Here, the first 13 community detection methods adopt the traditional methods to optimize the modularity function, and the other 17 community detection methods adopt the meta-heuristics evolutionary optimization approaches to optimize the modularity function, where GA is employed as an optimization strategy by MAGA-net, GATHB, MOGA, ECGA, CC-GA, and MOEA/D, DE is employed as an optimization strategy by DECD, CCDECD, IDDE, and CDEMO, and BA, ACO, PSO, FOA, STA, and SOS is employed as optimization strategies by the rest of the algorithms, respectively.…”

Section: ) Results On Small-scale Real-world Networkmentioning

confidence: 99%

Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm

2022

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In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clustering are promising for dynamic community detection, there is still room for further improvement in the snapshot quality and the temporal cost. In this study, a dynamic community detection algorithm based on optional pathway guide pity beetle algorithm (DYN-OPGPBA), which is a novel dynamic community detection method based on the framework of evolutionary clustering, is proposed. We propose an improved PBA for community detection of the network at the first time step, including a discrete search strategy based on adjacent nodes, a closeness-based community modification strategy and a crowded community split strategy. Compared with many representative static community detection methods, the proposed method has some superior detection accuracy. A neighbour vector competition-based individual update strategy and an external population size restriction mechanism are also proposed for community detection at subsequent time steps. Results show that DYN-OPGPBA has a better balance between snapshot quality and temporal cost than two representative dynamic community detection methods.

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Section: ) Results On Small-scale Real-world Networkmentioning

confidence: 99%

Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm

2022

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“…Various soft computing evolutionary heuristic strategies have also been applied to evolve communities by optimizing an objective function through selection of the fittest methodology. To optimize modularity Gach and Hao (2012) applied memetic evolutionary algorithm; Banati and Arora (2015) applied Group Search Optimization(GSO); Wang et al (2013) applied Differential Evolution; Hafez et al (2014) applied Artificial Bee Colony (ABC) optimization algorithm; Shang et al (2013) applied improved Genetic algorithm accompanied with simulated annealing as a local search; Gong et al (2013) proposed MODPSO by modifying Particle Swarm Optimization(PSO) algorithm and others (Cao et al, 2015;Gong et al, 2011;Liu et al, 2015;Pizzuti 2008). However all these methods were single objective in nature.…”

Section: Figure 1 Broad Categorization Of Community Detection Methodsmentioning

confidence: 99%

Multiobjective Group Search Optimization Approach for Community Detection in Networks

Arora

Banati

2016

International Journal of Applied Evolutionary Computation

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Various evolving approaches have been extensively applied to evolve densely connected communities in complex networks. However these techniques have been primarily single objective optimization techniques, which optimize only a specific feature of the network missing on other important features. Multiobjective optimization techniques can overcome this drawback by simultaneously optimizing multiple features of a network. This paper proposes MGSO, a multiobjective variant of Group Search Optimization (GSO) algorithm to globally search and evolve densely connected communities. It uses inherent animal food searching behavior of GSO to simultaneously optimize two negatively correlated objective functions and overcomes the drawbacks of single objective based CD algorithms. The algorithm reduces random initializations which results in fast convergence. It was applied on 6 real world and 33 synthetic network datasets and results were compared with varied state of the art community detection algorithms. The results established show the efficacy of MGSO to find accurate community structures.

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“…Z. Li et al [25] have proposed a multi-agent genetic algorithm for large-scale networks (MAGA-Net) to overcome local optima problem. Other time consumption, the major problems affects the performance than existing algorithms, especially, community detection method utilizing multi-swarm fruit fly optimization algorithm (CDMFOA) [26], that are slow convergence of GA, unguided mutation, no guarantee of finding global maxima, difficult fine tuning of GA parameters, and long training time.…”

Section: Problem Definition and Solutionmentioning

confidence: 99%

“…Choose smell best and location-The traditional FOA [26] utilized to compute the smellbest. Because the value of best smell may change in different fruit fly population, so the smellbest is used to keep the best smell so as to have a comparison with the maximal smell concentration in the next fruit fly population.…”

Section: Fruit Fly Optimization Algorithm Based On Differential Evolumentioning

confidence: 99%

Hybrid Optimization Algorithm for Community and Fraud Detection in Complex Networks for High Immunity Towards Link and Node Failures

Singh¹,

Singh²

2018

IJIES

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Abstract:The complex networks are offering a high resource of heterogeneous data and the proper and efficient analysis discovers the unknown information and relations in networks. Due to the huge number of users and nonfamiliar fraud detection system in complex networks, a lot of online frauds introduce to affects the networks. In this paper, we concentrate on both community and fraud detection to minimize the link and node failures in the complex networks. A hybrid optimization algorithm proposed for community and fraud detection in the complex networks (HCFD-Net). The first contribution is to detect the community based on fruit fly optimization algorithm with differential evolution (FOADE). The second contribution is that the fraud detection is achieved by contingency table terminology with multi-link metrics. The performance of the HCFD-Net is analyzed on different five real-world networks are Zachary's karate club, Bottlenose dolphins', American college football, American political books, and Amazon online purchase network. The simulation result shows that the proposed HCFD-NET perform very efficient than existing algorithms in terms of normalized mutual information (NMI) and network lifetime.

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Community Detection Utilizing a Novel Multi-swarm Fruit Fly Optimization Algorithm with Hill-Climbing Strategy

Cited by 20 publications

References 37 publications

Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm

Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm

Multiobjective Group Search Optimization Approach for Community Detection in Networks

Hybrid Optimization Algorithm for Community and Fraud Detection in Complex Networks for High Immunity Towards Link and Node Failures

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