Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks

Guo, Yuquan; Li, Xiongfei; Tang, Youxi; Li, Jun

doi:10.1155/2017/4143638

Cited by 11 publications

(4 citation statements)

References 46 publications

(57 reference statements)

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“…They are determined and replace the sources of food with the new sources of food discovered by scout bees. The best sources of food are found (Guo, Li, Tang & Li, 2017).…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Solving the Travelling Salesman Problem by Using Artificial Bee Colony Algorithm

Jamaluddin

Naziri

Mahmud

et al. 2022

JCRINN

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Travelling Salesman Problem (TSP) is a list of cities that must visit all cities that start and end in the same city to find the minimum cost of time or distance. The Artificial Bee Colony (ABC) algorithm was used in this study to resolve the TSP. ABC algorithms is an optimisation technique that simulates the foraging behaviour of honey bees and has been successfully applied to various practical issues. ABC algorithm has three types of bees that are used by bees, onlooker bees, and scout bees. In Bavaria from the Library of Traveling Salesman Problem, the distance from one city to another has been used to find the best solution for the shortest distance. The result shows that the best solution for the shortest distance that travellers have to travel in all the 29 cities in Bavaria is 3974km.

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“…They are determined and replace the sources of food with the new sources of food discovered by scout bees. The best sources of food are found (Guo, Li, Tang & Li, 2017).…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Solving the Travelling Salesman Problem by Using Artificial Bee Colony Algorithm

Jamaluddin

Naziri

Mahmud

et al. 2022

JCRINN

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“…The degree of association was used as heuristic information. Similarly, Guo et al [135] adapted ACO to find communities in CNs. The algorithm includes initialization, and three various of searching; employed bee, onlooker, and scout bee.…”

Section: B Community Detection Based On Meta-heuristic Algorithmsmentioning

confidence: 99%

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

et al. 2021

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Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, Parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions. INDEX TERMSCommunity detection; Deep learning; Complex networks; Meta-heuristic algorithms; Parallel computing.

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“…With the development of community detection algorithms, many evaluation parameters for community structure such as modularity and conductivity have been proposed [9]. As a result, there are many optimization-based algorithms proposed to optimize the objective functions based on these parameters, such as multiobjective genetic algorithm for community detection in networks (MOGA-NET) [10], heuristic artificial bee colony (HABC) [11], Order Statistics Local Optimization Method (OSLOM) [12], LouvainSprs [13], LouvainSgnf [14] and multi-objective discrete cuckoo search algorithm with local search (MDCL) [15]. Because of the intrinsic correlation between network structure and dynamical behaviors in the networks, many network dynamics-based algorithms utilize the dynamical characteristics of complex networks for community detection, e.g., random walks and diffusion on networks [16], maps of random walks on complex networks (Infomap) [17], the method using random walks (Walktrap) [18], the Label Propagation Algorithm (LPcopra) [19], and multiresolution community detection in large-scale networks (MSCD_HSLSW) [20].…”

Section: Related Workmentioning

confidence: 99%

Community Detection and Visualization in Complex Network by the Density-Canopy-Kmeans Algorithm and MDS Embedding

Wang

Long

et al. 2019

IEEE Access

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With the increasing availability of social networks and biological networks, detecting network community structure has become more and more important. However, most traditional methods for detecting community structure have limitations in dimension reduction or parameter optimization. In this paper, we propose a Density-Canopy-Kmeans clustering algorithm (DCK) to detect network community structure. Specifically, we define a novel distance metric, which integrates random distance and community structure coefficient based on the Jaccard distance. After applying the Multidimensional Scaling (MDS) dimension reduction, we cluster the nodes. KMEANS is combined with density clustering and canopy clustering to determine the optimal number of communities and the best initial seeds are determined to improve the accuracy and stability of the K-means algorithm. Compared with traditional community detection methods, our method has a higher classification accuracy and a better visualization effect. Thus, this method is effective for analyzing network communities. INDEX TERMS Network, community detection, the Density-Canopy-Kmeans clustering algorithm, MDS.

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Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks

Cited by 11 publications

References 46 publications

Solving the Travelling Salesman Problem by Using Artificial Bee Colony Algorithm

Solving the Travelling Salesman Problem by Using Artificial Bee Colony Algorithm

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

Community Detection and Visualization in Complex Network by the Density-Canopy-Kmeans Algorithm and MDS Embedding

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